HOW TO BETTER UTILIZE THE RAG SYSTEM

Retrieval-Augmented Generation (RAG) systems are a technology that utilizes text embeddings to build recommendation systems. It goes beyond simple search to find and provide semantically relevant information, and combines with LLM to generate more natural and useful answers.

RECENT ADVANCES IN AI TECHNOLOGY ARE MOVING SEARCH-BASED SYSTEMS AWAY FROM SIMPLE KEYWORD MATCHING AND TOWARD RECOMMENDING INFORMATION BASED ON SEMANTIC UNDERSTANDING. IT'S IMPORTANT TO REFLECT THE USER'S CONTEXT AND CREATE SEARCHES AND RESPONSES THAT ARE CONTEXTUALIZED, NOT JUST INFORMATIONAL.

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Inference complexity and purposefulness

The system becomes more complex when you want more complex reasoning. For example, the search and reasoning process will be different depending on whether the user wants contract information or the history of contract changes. If you don't account for these differences, performance can suffer.

ADDITIONALLY, WITHOUT METADATA, IT'S DIFFICULT TO PROVIDE APPROPRIATE RESPONSES. IF THE AI CAN'T INFER A DOCUMENT'S REVISION HISTORY OR CURRENT STATE, IT CAN REDUCE THE ACCURACY OF ITS ANSWERS.

Additionally, a large amount of information does not guarantee that it contains the information you need; in fact, a large amount of unnecessary information can make searching and reasoning more difficult.

SUMMARIZING IS ALSO AN INTERESTING ENDEAVOR. A GOOD SUMMARY CAN BE A WAY TO INCLUDE ENTITIES, MAINTAIN AN APPROPRIATE LENGTH, CONVEY NUANCE, AND EFFECTIVELY CONDENSE AND CONVEY THE NECESSARY INFORMATION. FOR A RAG SYSTEM TO WORK EFFECTIVELY, THE QUALITY OF SUMMARIES IS IMPORTANT, AND IT'S NOT JUST ABOUT CONDENSING INFORMATION, BUT ABOUT GETTING TO THE POINT WHILE PRESERVING MEANING.

ANOTHER EXAMPLE MIGHT BE GENERATING A FULL SUMMARY OF MEETING MINUTES AND ACTION ITEMS. IN SOME CASES, THE USER MIGHT WANT A SHORTER LIST OF ACTIONS, IN WHICH CASE A STRATEGY MIGHT BE TO SPLIT THE TASK AND GENERATE THE SUMMARY AND ACTION ITEMS SEPARATELY. THIS MEANS THAT AI SHOULDN'T JUST SUMMARIZE INFORMATION, BUT DELIVER RESULTS IN DIFFERENT FORMS TO SUIT THE USER'S PURPOSE.

When it comes to fine-tuning, you can use specialized tools to create and train thousands of examples, but you may find it more effective to take a step-by-step approach. It's important to take a step-by-step approach to improve your model's generalization performance and efficiently organize your training data for specific purposes.

You may also need to use Re-ranker because the documents retrieved may not necessarily match your intent exactly. Re-rankers re-evaluate the relevance of documents after the initial search and place the best information at the top. This helps the RAG system generate answers based on more accurate information.

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Difficulty collecting and storing data

THE KEY TO A RAG SYSTEM IS COLLECTING AND PROPERLY MANAGING RELIABLE DATA.

• You need a regular data update process to keep your information fresh.
• Without metadata, it can be difficult to infer the current state of a document.
• Just because there's a lot of data doesn't mean it necessarily contains the information you need.
• You need an efficient data management strategy, including chunking strategies, diverse data sources, and utilizing streaming data.

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Challenges of performance evaluation and continuous improvement

YOU SHOULD EVALUATE THE PERFORMANCE OF YOUR RAG SYSTEM AND CONTINUOUSLY IMPROVE IT, BUT THIS REQUIRES A LOT OF EFFORT.

• You may need a test dataset and an Eval mechanism to evaluate whether the retrieved documents are appropriate.
• Reflect user feedback to improve search and response quality.
• INCOMPLETE DATA MAKES IT DIFFICULT FOR AI TO GENERATE RELIABLE RESPONSES.
• It's even more important that it contains accurate information that is fit for purpose.

WHILE RAG SYSTEMS SEEM SIMPLE IN CONCEPT, THERE ARE MANY COMPLEXITIES IN THE ACTUAL DEPLOYMENT AND OPERATION. FROM DATA COLLECTION TO SEARCH, RESPONSE GENERATION, AND PERFORMANCE EVALUATION, THERE ARE TECHNICAL AND OPERATIONAL CHALLENGES. WHERE SPECIALIZED DOMAIN KNOWLEDGE IS REQUIRED, HUMAN INTERVENTION MAY BE NECESSARY TO COMPENSATE FOR THE LIMITATIONS OF AI.

IN ADDITION, DURING SUMMARIZATION AND DATA PROCESSING, YOU MAY NEED A STRATEGY TO SEPARATE TASKS BASED ON USER NEEDS. FOR EXAMPLE, WHEN GENERATING A SUMMARY OF MEETING MINUTES, YOU MAY NEED THE ABILITY TO GENERATE ACTION LISTS SEPARATELY, AND THIS STRUCTURED APPROACH CAN CONTRIBUTE TO EFFECTIVE RAG SYSTEM OPERATION.

When fine-tuning, it's important to take a step-by-step approach to building more sophisticated models and achieving optimal performance, rather than simply training on large amounts of data.

IN THE FUTURE, MORE SOPHISTICATED RAG SYSTEMS SHOULD BE DEVELOPED THROUGH CONTINUOUS IMPROVEMENT AND OPTIMIZATION.

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