In the field of Natural Language Processing (NLP), Retrieval Augmented Generation, or RAG, has attracted much attention lately. Breaking down documents into chunks, embedding those chunks, storing the embeddings, and then finding the closest match and adding it to the query context when receiving a query is a seemingly straightforward process. It would seem simple to get RAG to function well regularly in production, as many RAG components are already easily accessible, such as embedding models from OpenAI or Hugging Face and commercial vector databases for storing and searching embeddings.Â
However, this is not accurate as a basic RAG system can be easy to set up, but it can be considerably harder to make sure that it works well in practical applications. After testing with actual user requests and implementing RAG into end-to-end systems, real problems frequently surface. We wrote this article after getting inspired by this LinkedIn user’s post, which listed seven typical issues with production RAG systems, along with possible fixes.Â
Missing Content
The knowledge base’s missing information is one of the biggest problems. This happens when the pertinent context is missing, which makes the model give false answers rather than owning up to its ignorance.
Solutions
Data Cleaning: The first stage involves clearing the data of noise, superfluous information, and mistakes, including typos, misspellings, and grammatical errors. To make sure the knowledge base is as precise and complete as feasible, duplicates should also be removed.
Improved Prompting: An alternative strategy would be to tell the system to say “I don’t know†straight out if it doesn’t know the answer. Although not infallible, this method can assist in decreasing the number of inaccurate responses.
Incorrect Specificity
When the result is vague or lacks specificity, it can also be a common problem and necessitates further inquiries to get the facts straight.
Solutions
Advanced Techniques for Retrieval: Recursive retrieval, sentence window retrieval, small-to-big retrieval, and other advanced retrieval techniques can help extract more relevant and particular information, hence decreasing the need for follow-up inquiries.
Missed Top-Ranked Documents
The algorithm is sometimes unable to find the most pertinent papers because the right response is concealed in one that did not score well enough to be sent back to the user.
Solutions
Reranking: The system’s performance can be greatly enhanced by reranking retrieval results before forwarding them to the LLM. For this process, choosing the optimal embedding and reranked models is essential.
Hyperparameter Tuning: The retrieval process can be improved by adjusting the chunk size and similarity_top_k hyperparameters. The system can be optimized more easily by automating this tuning process with the use of tools like LlamaIndex.
Not in context
The issue is that documents that have the solution are occasionally obtained from the database, but they are not part of the context utilized to produce the solution. When a lot of papers are returned, and the system has trouble efficiently consolidating them, this problem frequently occurs.
Solutions
Trying Different Retrieval Strategies: To make sure that pertinent documents are included in the context, experiment with different retrieval strategies is conducted such as basic retrieval from each index, advanced retrieval and search, auto-retrieval, knowledge graph retrievers, and composed/hierarchical retrievers.
Perfect Embeddings: Optimising embeddings can also enhance the retrieved documents’ correctness and relevancy. Particularly helpful are step-by-step instructions for optimizing open-source embedding models, such as those found on LlamaIndex.
Incorrect Format
The issue is that the system occasionally produces output that is incorrectly formatted, such as a block of text being returned in place of a table.
Solutions
Improved Prompting/Instructions: It can be guaranteed that the output is in the intended format by making the request simpler and giving more precise instructions. Providing examples and posing follow-up queries can help make the system’s purpose even more clear.
Parsing Output: This problem can also be solved by implementing formatting guidelines and parsing techniques for LLM outputs. Guardrails and LangChain are examples of tools that provide output parsing modules that can be included in the system.
Not Extracted
The issue is that when there is too much noise or contradicting information in the context, the system can have trouble deriving the right response.
Solutions
Data Cleaning: Data cleansing is essential for lowering noise and enhancing the system’s capacity to extract the right response, much like it is for missing material.
Prompt Compression: The system can concentrate on the most pertinent data by compressing the context after the retrieval stage but before feeding it into the LLM. This procedure can be improved by putting strategies like LongLLMLingua as a node postprocessor into practice.
LongContextReorder: Optimising efficiency might also involve rearranging the retrieved nodes to position crucial information at the beginning or conclusion of the input context. The LongContextReorder technique particularly addresses the “lost in the middle†issue, in which crucial information is buried in the context and ignored by the system.
Incomplete Output
Even when the required information is available and present in the context, the system could nevertheless give an incomplete response.
Solutions
Query Transformations: Using query transformations can greatly improve the system’s reasoning power in order to solve this problem. To make sure the system completely comprehends the query and obtains all pertinent data, strategies including sub-questions, routing, query-rewriting, and query comprehension layers can be used.
In conclusion, although creating a RAG system may appear simple, making it function well in a real-world setting is significantly more difficult. The difficulties mentioned emphasize how crucial it is to do extensive testing and fine-tuning in order to handle the typical problems that occur. Developers can increase the resilience and dependability of RAG systems and make sure they function successfully in real-world applications by utilizing cutting-edge approaches and technologies.
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