Enter retrieval augmented generation (RAG)—an innovative approach that seamlessly integrates information retrieval with text generation. This powerful combination of retrieval and generation has the potential to revolutionize applications from customer service chatbots to intelligent research assistants.

Let’s briefly uncover the future of AI-powered language understanding and generation through the lens of retrieval augmented generation.

Key features and benefits of RAG

Here are five key features and benefits that will help you understand RAG better.

1. Current and up-to-date knowledge

RAG models rely on external knowledge bases to retrieve real-time and relevant information before generating responses. LLMs were trained at a specific time and on a specific set of data. RAG allows for responses to be grounded on current and additional data rather than solely depending on the model’s training set.

Benefit: RAG-based systems are particularly effective when the data required is constantly changing and being updated. By incorporating real-time data, RAG patterns expand the breadth of what can be accomplished with an application, including live customer support, travel planning, or claims processing.

For example, in a customer support scenario, a RAG-enabled system can quickly retrieve relevant and accurate product specifications, troubleshooting guides, or customer’s purchase history, allowing users to resolve their issues efficiently. This capability is crucial in customer-support applications—where accuracy is paramount—because it not only enhances the user experience and fosters trust but also encourages the continued use of the AI system, helping to increase customer loyalty and retention.

2. Contextual relevance

RAG excels in providing contextually rich responses by retrieving data that is specifically relevant to the user’s query. This is achieved through sophisticated retrieval algorithms that identify the most pertinent documents or data snippets from a vast, disparate data set.¹

Benefit: By leveraging contextual information, RAG enables AI systems to generate responses that are tailored to the specific needs and preferences of users. RAG also enables organizations to maintain data privacy, versus retraining a model owned by a separate entity, allowing data to remain where it lives. This is beneficial in scenarios such as legal advice or technical support.

For example, if an employee asks about their company’s policy on remote work, RAG can pull the latest internal documents that outline those policies, ensuring that the response is not only accurate but is also directly applicable to the employee’s context. This level of contextual awareness enhances the user experience, making interactions with AI systems more meaningful and effective.

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3. Reduction of hallucinations

RAG allows for controlled information flow, finely tuning the balance between retrieved facts and generated content to maintain coherence while minimizing fabrications. Many RAG implementations offer transparent source attribution—citing references for retrieved information and adding accountability—which are both crucial for responsible AI practices. This auditability not only improves user confidence but also aligns with regulatory requirements in many industries, where accountability and traceability are essential.

Benefit: RAG boosts trust levels and significantly improves the accuracy and reliability of AI-generated content, thus helping to reduce risks in high-stakes domains like legal, healthcare, and finance. This leads to increased efficiency in information retrieval and decision-making processes, as users spend less time fact-checking or correcting AI outputs.²

For example, consider a financial advisor research assistant powered by RAG technology. When asked about recent Security and Exchange Commission filings regarding a publicly traded company in the United States from EDGAR, the commission’s online database, the AI system retrieves information from the latest annual reports, proxy statements, foreign investment disclosures, and other relevant documents filed by the corporation. The RAG model then generates a comprehensive summary, citing specific documents and their publication dates. This not only provides the researcher with current, accurate information they can trust, but also offers clear references for further investigation—significantly accelerating the research process while maintaining high standards of accuracy.

4. Cost effectiveness

RAG allows organizations to use existing data and knowledge bases without extensive retraining of LLMs. This is achieved by augmenting the input to the model with relevant retrieved data rather than requiring the model to learn from scratch.

Benefit: This approach significantly reduces the costs associated with developing and maintaining AI systems. Organizations can deploy RAG-enabled applications more quickly and efficiently, as they do not need to invest heavily in training large models on proprietary data.³

For example, consider a small-but-rapidly growing e-commerce company specializing in eco-friendly garden supplies. As they grow, they face the challenge of efficiently managing and utilizing their expanding knowledge base without increasing operational costs. If a customer inquires about the best fertilizer for a specific plant, the RAG system can quickly retrieve and synthesize information from product descriptions, usage guidelines, plant zone specifications, and customer reviews to provide a tailored response.

In this way, RAG technology allows the business to leverage its existing product documentation, customer FAQs, and a scalable internal knowledge base where the RAG system expands with the business, without the cost or need for extensive AI model training or constant updates. By providing accurate and contextually sensitive responses, the RAG system reduces customer frustration and potential returns—indirectly saving costs associated with customer churn and product returns.

5. User productivity

RAG helps boost user productivity by enabling users to access precise, contextually relevant data quickly by effectively combining information retrieval with generative AI.⁴

Benefit: This streamlined approach reduces the time spent on data gathering and analysis, allowing decision-makers to focus on actionable insights and teams to automate time-consuming tasks.

For example, KPMG built ComplyAI, a compliance checker, wherein employees submit client documents and request that the application review them. The app reviews the documents and flags any legal standards or compliance requirements, then sends the analysis to the user who originally set up the task. The app handles the review and analysis, saving the requestor time and effort. Thus, the app allows the user to ramp up on the topic or issue in question much faster without requiring them to be a legal expert.

As a result, users are more likely to perceive the AI application as a helpful and integral part of their daily tasks, whether in a professional or personal context.

Get started using RAG to enhance LLMs

In summary, by leveraging the vast knowledge stored in external sources, RAG enhances the capabilities of LLMs, including improved accuracy, contextual relevance, reduced hallucinations, cost-effectiveness, and improved auditability. These features collectively contribute to the development of more reliable and efficient AI applications across various sectors. RAG-enhanced systems also help empower smaller-sized businesses to compete effectively with larger competitors while managing their growth in a cost-effective manner, without the need to hire additional staff or for substantial AI model updates and retraining.

To get started, use the following resources to start building RAG applications with Azure AI Foundry and use them with agents built using Microsoft Copilot Studio.

• Build a RAG application with Azure OpenAI Service and Azure AI Search.
• Watch our videos:
  • Retrieval Augmented Generation with Azure AI Search
  • OpenAI creates retrieval augmented generation features with Azure AI Search

• Read about retrieval augmented generation (RAG) in Azure AI Search.
• Read about enhancing responses from agents built using Copilot Studio with your index built in Azure AI Search.

Our commitment to Trustworthy AI

Organizations across industries are leveraging Azure AI and Microsoft Copilot capabilities to drive growth, increase productivity, and create value-added experiences.

We’re committed to helping organizations use and build AI that is trustworthy, meaning it is secure, private, and safe. We bring best practices and learnings from decades of researching and building AI products at scale to provide industry-leading commitments and capabilities that span our three pillars of security, privacy, and safety. Trustworthy AI is only possible when you combine our commitments, such as our Secure Future Initiative and our Responsible AI principles, with our product capabilities to unlock AI transformation with confidence. 

¹DataCamp, How to Improve RAG Performance: 5 Key Techniques with Examples, 2024.

² Lewis, P., Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, 2020.

³ Castro, P., Announcing cost-effective RAG at scale with Azure AI Search, Microsoft, 2024.

⁴ Hikov, A. and Murphy, L., Information retrieval from textual data: Harnessing large language models, retrieval augmented generation and prompt engineering, Ingenta Connect, Spring 2024.

The post 5 key features and benefits of retrieval augmented generation (RAG) appeared first on The Microsoft Cloud Blog.

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This article outlines and defines various practices used across the RAG pipeline—full-text search, vector search, chunking, hybrid search, query rewriting, and re-ranking.

What is full-text search?

Full-text search is the process of searching the entire document or dataset, rather than just indexing and searching specific fields or metadata. This type of search is typically used to retrieve the most relevant chunks of text from the underlying dataset or knowledge base. These retrieved chunks are then used to augment the input to the language model, providing context and information to improve the quality of the generated response.

Full-text search is often combined with other search techniques, such as vector search or hybrid search, to leverage the strengths of multiple approaches.

The purpose of full-text search is to:

• Allow the retrieval of relevant data from the complete textual content of a document or dataset.
• Enable the identification of documents that may contain the answer or relevant information, even if the specific query terms are not present in the metadata or document titles.

The process of implementing a full-text search involves the following techniques:

• Indexing—the full text of documents or dataset is indexed, often using inverted index structures that store and organize information that helps improve the speed and efficiency of search queries and retrieved results.
• Querying—when a user query is received, the full text of the documents or dataset is searched to find the most relevant information.
• Ranking—the retrieved documents or chunks are ranked based on relevance to the query, using techniques like term frequency inverse document frequency (TF-IDF) or BM25.

What is vector search?

Vector search retrieves stored matching information based on conceptual similarity, or the underlying meaning of sentences, rather than exact keyword matches. In vector search, machine learning models generate numeric representations of data, including text and images. Because the content is numeric rather than plain text, matching is based on vectors that are most similar to the query vector, enabling search matching for:

• Semantic or conceptual likeness (“dog” and “canine,” conceptually similar yet linguistically distinct).
• Multilingual content (“dog” in English and “hund” in German).
• Multiple content types (“dog” in plain text and a photograph of a dog in an image file).

With the rise of generative AI applications, vector search and vector databases have seen a dramatic rise in adoption, along with the increased number of applications using dialogue interactions and question/answer formats. Embeddings are a specific type of vector representation created by natural language machine learning models trained to identify patterns and relationships between words.

There are three steps in processing vector search:

1. Encoding—use language models to transform or convert text chunks into high-dimensional vectors or embeddings.
2. Indexing—store these vectors in a specialized database optimized for vector operations.
3. Querying—convert user queries into vectors using the same encoding method to retrieve semantically similar content.

Things to consider when implementing vector search:

• Selecting the right embedding model for your specific use case, like GPT or BERT.
• Balancing index size, search speed, and accuracy.
• Keeping vector representations up to date as the source data changes.

What is chunking?

Chunking is the process of dividing large documents and text files into smaller parts to stay under the maximum token input limits for embedding models. Partitioning your content into chunks ensures that your data can be processed by the embedding models and that you don’t lose information due to truncation.

For example, the maximum length of input text for the Azure OpenAI Service text-embedding-ada-002 model is 8,191 tokens. Given that each token is around four characters of text for common OpenAI models, this maximum limit is equivalent to around 6,000 words of text. If you’re using these models to generate embeddings, it’s critical that the input text stays below the limit.

Documents are divided into smaller segments, depending on:

• Number of tokens or characters.
• Structure-aware segments, like paragraphs and sections.
• Overlapping windows of text.

When implementing chunking, it’s important to consider these factors:

• Shape and density of your documents. If you need intact text or passages, larger chunks and variable chunking that preserves sentence structure can produce better results.
• User queries. Larger chunks and overlapping strategies help preserve context and semantic richness for queries that target specific information.
• Large language models (LLMs) have performance guidelines for chunk size. You need to set a chunk size that works best for all of the models you’re using. For instance, if you use models for summarization and embeddings, choose an optimal chunk size that works for both.

What is hybrid search?

Hybrid search combines keyword search and vector search results and fuses them together using a scoring algorithm. A common model is reciprocal rank fusion (RRF). When two or more queries are executed in parallel, RRF evaluates the search scores to produce a unified result set.

For generative AI applications and scenarios, hybrid search often refers to the ability to search both full text and vector data.

The process of hybrid search involves:

1. Transforming the query into a vector format.
2. Performing vector search to find semantically similar chunks.
3. Simultaneously conducting keyword search on the same corpus.
4. Combining and ranking results from both methods.

When implementing hybrid search, consider the following:

• Balancing the influence of each search method.
• Increased computational complexity compared to single-method search.
• Tuning the system to work well across diverse types of queries and content.
• Overlapping keywords to match when using question and answering systems, like ChatGPT.

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What is query rewriting?

Query rewriting is an important technique used in RAG to enhance the quality and relevance of the information retrieved by modifying and augmenting a provided user query. Query rewriting creates variations of the same query that are shared with the retriever simultaneously, alongside the original query. This helps remediate poorly phrased questions and casts a broader net for the type of knowledge collected for a single query.

In RAG systems, rewriting helps improve recall, better capturing user intent. It’s performed during pre-retrieval, before the information retrieval step in a RAG scenario.

Query rewriting can be approached in three ways:

1. Rules-based—using predefined rules and patterns to modify the query.
2. Machine learning-based—training models to learn how to transform queries based on examples.
3. Mixed—combining rules-based and machine learning-based techniques.

What is re-ranking?

Re-ranking, or L2 ranking, uses the context or semantic meaning of a query to compute a new relevance score over pre-ranked results. Post retrieval, a retrieval system passes search results to a ranking machine-learning model that scores the documents (or textual chunks) by relevance. Then, the top results of a limited, defined number of documents (top 50, top 10, top 3) are shared with the LLM.

Learn how to start building a RAG application

RAG systems employ various techniques to enhance knowledge retrieval and improve the quality of generated responses. These techniques work to provide language models with highly relevant context to generate accurate and informative responses.

To get started, use the following resources to start building a RAG application with Azure AI Foundry and use them with agents built using Microsoft Copilot Studio.

• Build a RAG application with Azure OpenAI Service and Azure AI Search.
• Watch our videos:
  • Retrieval augmented generation with Azure AI Search
  • OpenAI creates retrieval augmented generation features with Azure AI Search

• Read about retrieval augmented generation (RAG) in Azure AI Search.
• Read about enhancing responses from agents built using Copilot Studio with your index built in Azure AI Search.

Our commitment to Trustworthy AI

Organizations across industries are leveraging Azure AI Foundry and Microsoft Copilot Studio capabilities to drive growth, increase productivity, and create value-added experiences.

We’re committed to helping organizations use and build AI that is trustworthy, meaning it is secure, private, and safe. We bring best practices and learnings from decades of researching and building AI products at scale to provide industry-leading commitments and capabilities that span our three pillars of security, privacy, and safety. Trustworthy AI is only possible when you combine our commitments, such as our Secure Future Initiative and our Responsible AI principles, with our product capabilities to unlock AI transformation with confidence. 

Azure remains steadfast in its commitment to Trustworthy AI, with security, privacy, and safety as priorities. Check out the 2024 Responsible AI Transparency Report.

The post Common retrieval augmented generation (RAG) techniques explained appeared first on The Microsoft Cloud Blog.

An SLM focuses on specific AI tasks that are less resource-intensive, making them more accessible and cost-effective.¹ SLMs can respond to the same queries as LLMs, sometimes with deeper expertise for domain-specific tasks and at a much lower latency, but they can be less accurate with broad queries.² LLMs are an excellent choice for building your own enterprise custom agent or generative AI applications because of how capable they are.

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Compare SLMs versus LLMs

Here are some criteria for each model type shown side-by-side to help you evaluate at a glance before diving deep into your due diligence and choosing one approach over another.

SLM and LLM functions

When comparing functions for small versus large language models, you should consider the balance between cost and performance. Smaller models typically require less computational power, reducing costs, but might not be well-suited for more complex tasks. Larger models offer superior accuracy and versatility but come with higher infrastructure and operational expenses. Evaluate your specific needs, like real-time processing, task complexity, and budget constraints, to make an informed choice.

You should also consider that SLMs can be fine-tuned to perform well in required tasks. Fine-tuning is a powerful tool to tailor advanced SLMs to your specific needs, using your own proprietary data. By fine-tuning an SLM, you can achieve a high level of accuracy for the particular use cases you require without needing to deploy an LLM that could be more expensive.  

For more complex tasks with a lot of edge cases, such as natural language queries or teaching a model to speak in a specific voice or tone, fine-tuning LLMs is a better solution. 

SLM and LLM features

Also be sure to consider features like computational efficiency, scalability, and accuracy. Smaller models often offer faster processing and lower costs, while larger models provide enhanced understanding and performance on complex tasks but require more resources. Evaluate your specific use cases and resource availability to help make an informed decision. 

SLM and LLM use cases

Carefully consider your specific use cases when comparing language models. Smaller models are ideal for tasks that require quick responses and lower computational costs, such as basic customer service chatbots or simple data extraction. On the other hand, large language models excel in more complex tasks requiring deep comprehension and nuanced responses, like advanced content generation or sophisticated data analysis. Aligning the model size with your specific business needs ensures you achieve both efficiency and effectiveness. 

SLM and LLM limitations

It’s also essential to consider limitations like computational requirements and scalability. Smaller models can be cost-effective and faster, but might not have the same nuanced understanding and depth of larger models. Larger models require significant computational resources, which can lead to higher costs and longer processing times. Balance these limitations against your specific use cases and available resources. 

This article touches on at-a-glance comparative information demonstrating the power and benefits of both SLMs and LLMs. With AI innovation accelerating at an intense pace involving different languages and scenarios, this rapid development will be sure to push the limits of both types of models—resulting in better, cheaper, and faster versions of current AI systems. This is particularly true for startups with limited resources where SLMs like Phi-3 open models will likely be the preferred, practical choice to leverage AI for their use cases.

Explore more resources on SLMs and LLMs

• Watch our AI in a Minute video about LLMs
• Explore our training: Introduction to large language models
• Read about the benefits of using SLMs in certain scenarios

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Our commitment to Trustworthy AI

Organizations across industries are leveraging Azure AI and Microsoft Copilot capabilities to drive growth, increase productivity, and create value-added experiences. 

We’re committed to helping organizations use and build AI that is trustworthy, meaning it is secure, private, and safe. We bring best practices and learnings from decades of researching and building AI products at scale to provide industry-leading commitments and capabilities that span our three pillars of security, privacy, and safety. Trustworthy AI is only possible when you combine our commitments, such as our Secure Future Initiative and our responsible AI principles, with our product capabilities to unlock AI transformation with confidence.      

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• See the latest Azure OpenAI Service news.
• Read more in our Azure AI services documentation.
• Read the latest AI and machine learning blogs.
• Listen to the podcast on Phi-3 with lead Microsoft researcher Sebastien Bubeck.

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¹Small Language Models (SLMs): The Next Frontier For The Enterprise, Forbes.

²Small Language Models vs. Large Language Models: How to Balance Performance and Cost-effectiveness, instinctools.

³Big is Not Always Better: Why Small Language Models Might Be the Right Fit, Intel.

The post Explore AI models: Key differences between small language models and large language models appeared first on The Microsoft Cloud Blog.

Large language models (LLMs) are AI systems based on transformer architectures and trained on vast amounts of text data to understand and generate human-like text. Using deep learning techniques, LLMs process and produce accurate responses rapidly. Deep learning is a subset of machine learning that uses multi-layered neural networks to simulate the complex decision-making power of the human brain.

Large language models are trained on a massive volume of data, and once properly trained, they have a broad applicability for a range of natural language processing and machine learning applications. LLMs are typically multiple billions of parameters in size, making them five to ten times larger than small language models (SLMs).

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What can LLMs do?

Large language models (LLMs) offer significant benefits across various industries by automating and enhancing numerous tasks involving natural language processing. These AI-powered tools can rapidly analyze vast amounts of text data, generate human-like content, and provide intelligent responses to queries. However, always keep in mind that any content created by AI models and used in final deliverables must not infringe on copyrights or intellectual property rights of the original owners.

• In business, LLMs may improve customer service through chatbots, streamline document analysis, and assist with market research.
• In healthcare, LLMs may assist clinicians with reviewing medical literature and clinical documentation.
• In education, LLMs may help teachers create personalized learning materials and provide instant tutoring assistance for their students.
• In the legal industry, LLMs may help law firms with contract analysis and legal research.

Additionally, LLMs can help support content ideation for marketing, journalism, and creative industries.

Let’s take a brief tour through the world of large language models.

5 key features and benefits of LLMs

While there are many benefits of large language models, here are five to consider:

1. Natural language understanding

The model can interpret context, detect sentiment, and understand idiomatic expressions and colloquialisms. It can often infer unstated information and respond appropriately to ambiguous queries. Also, LLMs can combine information from various sources in their training data to answer complex questions, solve problems creatively, translate languages, and even assist in research and innovation.

Benefit: LLMs can comprehend context, nuance, and intent in the text that was input into it, which allows for more intuitive human-computer interaction. The large language model enables the discovery of new insights and connections across diverse fields. It also powers more intelligent search engines that provide direct, human-like answers to queries rather than just links to relevant pages.

2. Versatile multimodal generation

LLMs can produce coherent and contextually appropriate outputs in multiple styles, languages, and formats—from poems and stories to emails, technical reports, and even spoken language. With advancements in multimodality, these models now extend beyond text to support speech, images, and other forms of media. This facilitates global communication, broadens access to information, performs translation tasks, question-answering, generating code with minimal additional training, and even understanding code-switching within conversation or between different media types.

Benefit: Synthesizing knowledge across text, speech, and other modalities saves time and resources in content creation across various domains. The models can analyze and determine sentiment or emotional tone in both text and speech, which is valuable for market research, customer feedback reviews, and even personalized interactions like voice-based assistants or multimedia content generation.

3. Code generation and analysis

Large language models can produce code as well as text. For example, LLMs can assist developers by generating code snippets, functions, or even entire programs based on natural language descriptions. They can also analyze existing codebases to help identify bugs, suggest optimizations, and provide explanations of complex code sections, effectively serving as an AI-powered coding assistant. In addition, LLMs can assist developers with:

• Building applications
• Auto-completing code
• Finding errors in code
• Analyzing and debugging software code
• Offering round-the-clock assistance without fatigue
• Creating test cases based on function specifications
• Creating entire code blocks in various programming languages
• Suggesting appropriate design patterns for given problems
• Suggesting improvements for code readability and maintainability
• Identifying security issues across multiple programming languages

Benefit: Developers can tailor the code to specific industries and use cases, thus adapting the model to specialized domains like healthcare, law, marketing, customer service, scientific research, and finance.

4. Task-specific without fine-tuning

With their massive knowledge base, LLMs can perform tasks such as summarization, translation, question-answering, and code generation with minimal additional training. The LLMs can be retrained periodically to respond in a more human-like manner, incorporate new data, and improve performance. 

Benefit: Reduces the need for specialized models for different tasks since they are so capable. LLMs excel at generating content that sounds natural, across multiple subject areas, with high accuracy.

5. Scalability and efficiency

LLMs can process long-form content or analyze extensive documents in parallel, leveraging graphics processing unit (GPU) capabilities for faster training and inference. This allows for efficient handling of large-scale language tasks and rapid generation of responses.

Benefit: Easily handles increased workloads and adapts to growing business needs. They can analyze large volumes of text data to extract insights and patterns, aiding in decision-making processes and boosting productivity.

Use LLMs to build comprehensive AI solutions to revolutionize industries

LLMs have revolutionized natural language processing by offering robust capabilities for understanding and generating human-like text. Despite their significant advancements, there are still some limitations. To ensure their ethical and appropriate use across various sectors, continuous improvements are necessary as we move forward.

LLMs can be used with other Microsoft Azure AI products to build advanced and comprehensive solutions to suit most industries. Their features and benefits make them an attractive option for businesses seeking to enhance natural language processing capabilities across various applications—from customer service to content creation and software development.

The ability of large language models to understand context, generate coherent text, and adapt to specific domains makes them versatile and valuable tools that are not only applicable in fields beyond just language processing—such as software development, data science, decision support systems, and creative industries—but that organizations can rely on to boost productivity, efficiency, and innovation across sectors.

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Our commitment to responsible AI

Organizations across industries are leveraging Azure OpenAI Service and Microsoft Copilot services and capabilities to drive growth, increase productivity, and create value-added experiences. From advancing medical breakthroughs to streamlining manufacturing operations, our customers trust that their data is protected by robust privacy protections and data governance practices. As our customers continue to expand their use of our AI solutions, they can be confident that their valuable data is safeguarded by industry-leading data governance and privacy practices in the most trusted cloud on the market today. 

At Microsoft, we have a long-standing practice of protecting our customers’ information. Our approach to responsible AI is built on a foundation of privacy, and we remain dedicated to upholding core values of privacy, security, and safety in all our generative AI products and solutions.

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The post 5 key features and benefits of large language models appeared first on The Microsoft Cloud Blog.

Bigger is not always necessary in the rapidly evolving world of AI, and that is true in the case of small language models (SLMs). SLMs are compact AI systems designed for high volume processing that developers might apply to simple tasks. SLMs are optimized for efficiency and performance on resource-constrained devices or environments with limited connectivity, memory, and electricity—which make them an ideal choice for on-device deployment.¹

Researchers at The Center for Information and Language Processing in Munich, Germany found that “… performance similar to GPT-3 can be obtained with language models that are much ‘greener’ in that their parameter count is several orders of magnitude smaller.”² Minimizing computational complexity while balancing performance with resource consumption is a vital strategy with SLMs. Typically, SLMs are sized at just under 10 billion parameters, making them five to ten times smaller than large language models (LLMs).

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3 key features and benefits of SLMs

While there are many benefits of small language models, here are three key features and benefits.

1. Task-specific fine-tuning

An advantage SLMs have over LLMs is that they can be more easily and cost-effectively fine-tuned with repeated sampling to achieve a high level of accuracy for relevant tasks in a limited domain—fewer graphics processing units (GPUs) required, less time consumed. Thus, fine-tuning SLMs for specific industries, such as customer service, healthcare, or finance, makes it possible for businesses to choose these models for their efficiency and specialization while at the same time benefiting from their computational frugality.

Benefit: This task-specific optimization makes small models particularly valuable in industry-specific applications or scenarios where high accuracy is more important than broad general knowledge. For example, a small model fine-tuned for an online retailer running sentiment analysis in product reviews might achieve higher accuracy in this specific task than if they deployed a general-purpose large model.

2. Reduced parameter count

SLMs have a lower parameter count than LLMs and are trained to discern fewer intricate patterns from the data they work from. Parameters are a set of weights or biases used to define how a model handles and interprets information inputs before influencing and producing outputs. While LLMs might have billions or even trillions of parameters, SLMs often range from several million to a few hundred million parameters.

Here are several key benefits derived from a reduced parameter count:

• This significant reduction in size allows them to fit into limited-memory devices like smartphones, embedded systems, or Internet of Things (IoT) devices such as smart home appliances, healthcare monitors, or certain security cameras. The smaller size is cost effective too, because it means SLMs can be more easily integrated into applications without requiring substantial storage space or powerful server hardware.
• The lower latency leads to a quicker turnaround between input and output, which is ideal in scenarios such as real-time applications and environments where immediate feedback is necessary. Rapid responses help maintain user interest and can increase the overall experience with AI-powered applications.
• With fewer parameters to process, SLMs can generate responses much more quickly than their larger counterparts. This speed is crucial for applications that require real-time or near-real-time interactions, such as chatbots, voice assistants, or translation services.
• Low latency means queries are processed locally with near-instantaneous responses, making SLMs ideal solutions for time-sensitive applications like interactive customer support systems. Minimal on-device processing helps reduce the risk of data breaches, helps ensure information remains under organizational control, and aligns well with stringent data protection regulations, often found in the public sector as well as those proposed by the General Data Protection Regulation (GDPR). Plus, SLMs running at the edge helps ensure faster, more reliable performance, especially in scenarios where internet connectivity may be limited or unreliable. And devices with limited battery power or processing capabilities, such as low-end smartphones, can operate efficiently, thus extending their operational time between charges.

3. Enterprise-grade hosting on Microsoft Azure

Look for a small language model that provides streamlined full-stack development and hosting across static content and serverless application programming interfaces (APIs) that empower your development teams to scale productivity—from source code through to global high availability.

Benefit: For example, Microsoft Azure hosting for your globally deployed network enables faster page loads, enhanced security, and helps increase worldwide delivery of your cloud content to your users with minimal configuration or copious code required. Once your development team enables this feature for all required production applications in your ecosystem, we will then migrate your live traffic (at a convenient time for your business) to our enhanced global distributed network with no downtime.

Use SLMs as efficient and cost-effective AI solutions

To recap, when deploying an SLM for cloud-based services, smaller organizations, resource constrained environments, or smaller departments within larger enterprises, the main advantages are:

• Streamlined monitoring and maintenance
• Increased user control over their data
• Improved data privacy and security
• Reduced computational needs
• Reduced data retention
• Lower infrastructure
• Functions offline

These features and benefits mentioned above make small language models such as the Phi model family and GPT-4o mini on Azure AI attractive options for businesses seeking efficient and cost-effective AI solutions. It is worth noting that these compact yet powerful tools play a role in democratizing AI technology, enabling even smaller organizations to leverage advanced language processing capabilities.

Because of their different advantages, many organizations find the best solution is to use a combination of SLMs and LLMs to suit their needs. Choose SLMs over LLMs when processing specific language and vision tasks, more focused training is needed, or you are managing multiple applications—especially where resources are limited or where specific task performance is prioritized over broad capabilities.

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Our commitment to responsible AI

Organizations across industries are leveraging Microsoft Azure OpenAI Service and Microsoft Copilot services and capabilities to drive growth, increase productivity, and create value-added experiences. From advancing medical breakthroughs to streamlining manufacturing operations, our customers trust that their data is protected by robust privacy protections and data governance practices. As our customers continue to expand their use of our AI solutions, they can be confident that their valuable data is safeguarded by industry-leading data governance and privacy practices in the most trusted cloud on the market today. 

At Microsoft, we have a long-standing practice of protecting our customers’ information. Our approach to responsible AI is built on a foundation of privacy, and we remain dedicated to upholding core values of privacy, security, and safety in all our generative AI products and solutions.

Learn more about Azure’s Phi model

• Learn more about the Phi model family.
• Read about how you can boost your AI with Azure’s new Phi model.
• Listen to the podcast on Phi-3 with lead Microsoft researcher Sebastien Bubeck.

Learn more about AI solutions from Microsoft

• Explore Microsoft AI solutions to fuel your AI transformation.
• Explore how customers are putting Microsoft AI to work.
• Watch this video to learn more about the Azure AI model catalog.

¹MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices, Cornell University.

²It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners, The Center for Information and Language Processing in Munich Germany.

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