Key RAG Fine Tuning Strategies for Improved Performance

Looking to improve your RAG fine tuning skills? This guide breaks down key strategies that will help you achieve better performance and results.

May 15, 2024

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Key RAG Fine Tuning Strategies for Improved Performance

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If you're interested in making your content more dynamic and engaging, the concept of RAG fine tuning is a great way to boost your efforts. The strategy focuses on improving the use of Retrieval Augmented Generation for content generation. RAG fine tuning is a powerful tool that can help you create truly unique content that resonates with your audience. By learning the ins and outs of this technique, you'll be able to engage your readers in new and exciting ways. Fine tuning your content generation process with RAG fine tuning can make a big difference in the success of your campaigns.

What Is Retrieval Augmented Generation (RAG)?

Retrieval augmented generation, or RAG, is an architectural approach that can improve the efficacy of large language model (LLM) applications by leveraging custom data. This is done by retrieving data/documents relevant to a question or task and providing them as context for the LLM. RAG has shown success in support chatbots and Q&A systems that need to maintain up-to-date information or access domain-specific knowledge. RAG to LLMs is like giving them a new superpower. They can now access any information they need to provide highly accurate and relevant responses.

Challenges Solved by Retrieval Augmented Generation

1. LLM models do not know your data

LLMs use deep learning models and train on massive datasets to understand, summarize and generate novel content. Most LLMs are trained on a wide range of public data so one model can respond to many types of tasks or questions.

Once trained, many LLMs do not have the ability to access data beyond their training data cutoff point. This makes LLMs static and may cause them to respond incorrectly, give out-of-date answers or hallucinate when asked questions about data they have not been trained on.

2. AI applications must leverage custom data to be effective

For LLMs to give relevant and specific responses, organizations need the model to understand their domain and provide answers from their data vs. giving broad and generalized responses.

For example, organizations build customer support bots with LLMs, and those solutions must give company-specific answers to customer questions. Others are building internal Q&A bots that should answer employees' questions on internal HR data. How do companies build such solutions without retraining those models?

Solution: Retrieval augmentation is now an industry standard

An easy and popular way to use your own data is to provide it as part of the prompt with which you query the LLM model. This is called retrieval augmented generation (RAG), as you would retrieve the relevant data and use it as augmented context for the LLM.

Instead of relying solely on knowledge derived from the training data, a RAG workflow pulls relevant information and connects static LLMs with real-time data retrieval.

RAG Use Cases

Question and answer chatbots

Incorporating LLMs with chatbots allows them to automatically derive more accurate answers from company documents and knowledge bases. Chatbots are used to automate customer support and website lead follow-up to answer questions and resolve issues quickly

Search augmentation

Incorporating LLMs with search engines that augment search results with LLM-generated answers can better answer informational queries and make it easier for users to find the information they need to do their jobs.

Knowledge engine

Ask questions on your data (e.g., HR, compliance documents): Company data can be used as context for LLMs and allow employees to get answers to their questions easily, including HR questions related to benefits and policies and security and compliance questions.

3 Main Components of a Retrieval Augmented Generation System

1. Pre-trained LLM

The pre-trained Large Language Model (LLM) is the central engine in a retrieval-augmented generation (RAG) setup. This system generates text, images, audio, and video outputs, making it a fundamental component for processing information and responses. It operates based on its pre-existing knowledge and processing prowess, allowing it to generate responses and content with remarkable accuracy and efficiency.

2. Vector Search

The retrieval system, also known as vector search or semantic search, plays a critical role in the RAG setup. This component is responsible for retrieving relevant information from an external knowledge database to support the LLM. By identifying and extracting data based on vector embeddings, the retrieval system provides essential inputs to enhance the final output generated by the LLM.

3. Vector Embeddings

Vector embeddings, often simply referred to as vectors, are numerical representations of data that capture the semantic essence or underlying meaning of the input. This array of float values represents various dimensions of the data, allowing for a deeper understanding and interpretation by the RAG system. The use of vector embeddings significantly improves the ability to process and generate information more accurately.

4. Orchestration

Orchestration, sometimes called the fusion mechanism, is the component responsible for merging the output of the LLM with the information retrieved by the vector search system. By combining these two sets of data, the orchestration mechanism generates the final output, presenting a comprehensive response or content based on the synthesized inputs. This integration ensures that the content generated is nuanced, relevant, and meaningful.

By leveraging these crucial components, a RAG setup can optimize the process of information retrieval and generation. This system's ability to find, analyze, and generate content is driven by the seamless interaction between the pre-trained LLM, vector search, vector embeddings, and orchestration mechanisms. When configured effectively, a RAG system can yield highly accurate and contextually relevant responses and outputs that meet the demands of complex queries and information-processing tasks.

Enhancing Internal Operations with ChatBees' Low-Code RAG Solution

ChatBees optimizes RAG for internal operations like customer support, employee support, etc., with the most accurate response and easily integrating into their workflows in a low-code, no-code manner. ChatBees' agentic framework automatically chooses the best strategy to improve the quality of responses for these use cases. This improves predictability/accuracy enabling these operations teams to handle higher volume of queries.

More features of our service:

Serverless RAG

Simple, Secure and Performant APIs to connect your data sources (PDFs/CSVs, Websites, GDrive, Notion, Confluence)

Search/chat/summarize with the knowledge base immediately

No DevOps is required to deploy and maintain the service

Use cases

Onboarding

Quickly access onboarding materials and resources be it for customers or internal employees like support, sales, or research team.

Sales enablement

Easily find product information and customer data

Customer support

Respond to customer inquiries promptly and accurately

Product & Engineering

Quick access to project data, bug reports, discussions, and resources, fostering efficient collaboration.

Try our Serverless LLM Platform today to 10x your internal operations. Get started for free, no credit card required — sign in with Google and get started on your journey with us today!

Rag Pipeline

Rag Rating

Rag Workflow

Rag Llm

How Does Rag Work

Where Retrieval Augmented Generation Falls Short

When fine-tuning RAG systems, one of the main challenges is the dependency on the quality and scope of external data sources. The effectiveness of these systems heavily relies on having access to accurate and comprehensive external knowledge. If the data sources used are not reliable or lack relevant information, the performance of the RAG system will be negatively impacted. The specific domain expertise needed to understand and interpret the retrieved information can pose a challenge.

The system must be able to differentiate between similar documents or questions that may only be discernible to a specialist in the field. This issue can be exacerbated if the model is limited in its ability to recognize domain-specific jargon or nuances. Another challenge arises when integrating the retrieved information with generative models. The generative model may struggle with interpreting abbreviations, following instructions accurately, or maintaining proper formatting. This integration process needs to be seamless for the system to provide accurate and relevant responses.

Evaluating RAG System Performance

To evaluate the performance of a RAG system, various criteria need to be established to identify any issues and track improvements. Some key evaluation points include document relevance, reranking relevance, correctness, and hallucination. Document relevance assesses whether the retrieved documents contain relevant data to the query. Reranking relevance determines if the reranked results are more relevant than the original ones.

Correctness evaluates if the model provides the correct answer based on the supplied documents. Hallucination looks at whether the model adds information not present in the documents. Establishing a grading rubric is essential for consistency, especially when multiple individuals are involved in the assessment. By utilizing these evaluation criteria, it becomes easier to pinpoint areas for improvement and measure the effectiveness of fine-tuning efforts in the RAG system.

What Is RAG LLM

RAG LLM Meaning

Rag Model Llm

Rag Use Case

Llm Tech Stack

Rag Nlp

Rag Api

Rag Stack

Rag Systems

Rag Evaluation

Rag Service

Rag Use Cases

Rag Software

RAG Architecture LLM

LLM Rag Meaning

RAG LLM Example

RAG Fine Tuning Strategies to Optimize Performance

Embeddings: Enhancing Document Relevance Scores

If the document relevance score is low, your model isn’t returning the documents that contain the answer to the question or is producing a lot of non-relevant data. An embedding model transforms text into a vector, essentially condensing information into a compressed format. In RAG systems, datasets are chunked into smaller segments, encoded into vectors via the model, and stored in a vector database.

When a question is encoded, the resulting vector should ideally align with the document vector that holds the answer. To fine-tune embedding models, create datasets of question and document pairs. These pairings can be either positive (document answers the question) or negative (document does not answer the question). Utilize embedding models from libraries such as SentenceTransformers and follow specific guidelines for fine-tuning.

Reranker: Improving Relevance Ranking

In RAG systems, the Reranker reorders an initial list of potential matches. The core function of the Reranker differs from the embedding model. While embeddings compress information into vectors for similarity matching, the Reranker computes similarity scores based on uncompressed versions of the question and answer.

This method ensures higher quality similarity calculation but with increased computational requirements. The Reranker may also work in conjunction with other search systems, such as classic word matching. If the Reranker component underperforms, consider fine-tuning with task-specific datasets of question-and-answer pairs similar to the embedding model fine-tuning approach.

Large Language Model (LLM): Optimizing Model Performance

If the LLM struggles to answer questions about task-specific data in RAG systems, consider testing different LLMs to identify the best performer. Initiate with large models like GPT-4, and then explore open-source models if cost or data security concerns arise. LLMs are generally pre-trained on extensive datasets to predict the next token in a text sequence.

Post pre-training, supervised fine-tuning or reinforcement learning shapes LLMs for specific tasks. Accessing training data can unveil the prompt style used to imbue RAG capabilities in LLMs. Experiment with similar prompt styles for enhanced performance.

Fine-Tuning as a Last Resort

If other methods like prompt engineering fail to enhance RAG system performance, fine-tuning could be the solution. Leverage the effectiveness of GPT-4 to generate answers, which can then be used for fine-tuning smaller models. This approach minimizes data collection efforts. If using human-written data for fine-tuning is the only viable option, prepare for potential costs.

Training data is crucial for successful LLM models, elevating a good model to exceptional accuracy levels. While creating ideal training data presents challenges, it is essential for achieving optimal RAG system performance.

Embracing Retrieval Augmented Fine-Tuning (RAFT)

Retrieval Augmented Fine-Tuning (RAFT) is a cutting-edge technique that adds a new dimension to RAG (Retrieval Augmented Generation) systems, allowing for further enhancement and optimization. In a nutshell, RAFT is a refined method of fine-tuning that operates by training the model to ignore irrelevant retrieved documents that do not contribute to answering a specific question.

This process helps to eliminate distractions and ensures that the model focuses only on the most relevant information when generating responses. RAFT requires the accurate identification and quotation of relevant segments from relevant documents to address a particular query. RAFT leverages a chain-of-thought-style response to further refine the model's reasoning abilities, enhancing the quality and accuracy of the generated answers.

How RAFT modifies the standard RAG approach to achieve better integration and performance

In standard RAG, a model retrieves a few documents from an index that are likely to contain the answer to a given query. Traditional RAG approaches may not always filter out irrelevant documents effectively, leading to decreased accuracy and model performance. The introduction of RAFT refines this process significantly by training the model to overlook documents that do not contribute meaningfully to the response.

By doing so, RAFT minimizes the impact of irrelevant information, which can sometimes lead to the generation of inaccurate answers. This method ensures that the model focuses solely on the most relevant information from the retrieved documents, enhancing its ability to generate accurate, contextually appropriate responses. RAFT essentially bridges the gap between traditional RAG and specialized fine-tuning, providing a practical and effective way to refine large language models for domain-specific applications.

Use ChatBees’ Serverless LLM to 10x Internal Operations

ChatBees is a revolutionary tool that optimizes RAG for internal operations, such as customer support and employee assistance. By providing the most accurate responses and seamlessly integrating into workflows with a low-code, no-code approach, ChatBees simplifies the user experience.

The agentic framework of ChatBees automatically selects the most effective strategy to enhance response quality in these scenarios. This leads to improved predictability and accuracy, allowing operations teams to manage higher query volumes effectively.

The Many Benefits of ChatBees for Internal Operations

ChatBees offers a range of features designed to streamline internal operations. One such feature is the Serverless RAG, which provides simple, secure, and high-performing APIs to connect various data sources, such as PDFs/CSVs, websites, GDrive, Notion, or Confluence. By enabling users to search, chat, and summarize content directly from their knowledge base, ChatBees eliminates the need for DevOps support when deploying and maintaining the service.

The Diverse Use Cases of ChatBees

ChatBees is a versatile platform that can be applied to a variety of operational challenges. For instance, in onboarding, teams can rapidly access onboarding materials and resources for customers or internal employees in roles such as support, sales, or research. In sales enablement, ChatBees allows quick retrieval of product information and customer data.

For customer support teams, responding promptly and accurately to inquiries is made easier. In product and engineering functions, ChatBees facilitates swift access to project data, bug reports, discussions, and resources, fostering efficient collaboration among team members.

Experience Operational Excellence with ChatBees' Serverless LLM Platform

To experience the transformative power of ChatBees in your internal operations, consider trying the Serverless LLM Platform today. By leveraging this innovative tool, you can enhance your operational efficiency tenfold.

There's no need for a credit card to get started – simply sign in with Google and embark on a journey towards operational excellence with ChatBees.