The Challenges of Implementing Retrieval Augmented Generation (RAG) in Production
Missing Content
Data Cleaning: Clear the data of noise, superfluous information, and mistakes to ensure precision and completeness.
Improved Prompting: Instruct the system to say “I don’t know” to reduce inaccurate responses.
Incorrect Specificity
Advanced Techniques for Retrieval: Use advanced retrieval techniques to extract more relevant and specific information.
Missed Top-Ranked Documents
Reranking: Enhance system performance by reranking retrieval results before forwarding them to the LLM.
Hyperparameter Tuning: Improve the retrieval process by adjusting hyperparameters such as chunk size and similarity_top_k.
Not in Context
Trying Different Retrieval Strategies: Experiment with different retrieval strategies to ensure pertinent documents are included in the context.
Perfect Embeddings: Optimize embeddings to enhance the correctness and relevancy of retrieved documents.
Incorrect Format
Improved Prompting/Instructions: Guarantee the output is in the intended format by providing clearer instructions.
Parsing Output: Implement formatting guidelines and parsing techniques for LLM outputs.
Not Extracted
Data Cleaning: Lower noise and enhance the system’s capacity to extract the right response.
Prompt Compression: Concentrate on the most pertinent data by compressing the context after the retrieval stage.
LongContextReorder: Rearrange the retrieved nodes to position crucial information at the beginning or conclusion of the input context.
Incomplete Output
Query Transformations: Use query transformations to improve the system’s reasoning power and obtain all pertinent data.
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