Seven Failure Points When Engineering a Retrieval Augmented Generation System


RAG system aim to

  1. Reduce the problem of hallucinated responses from LLMs
  2. Link sources/references to generated responses
  3. Remove the need for annotating documents with meta-data.


  1. Validation of a RAG system is only feasible during operation
  2. The robustness of a RAG system evolves rather than designed in at the start.

Index process

  • If the chunks are too small certain questions cannot be answered, if the chunks are too long then the answers include generated noise.

Query process

  • Token limit or rate limit, we need to chain prompts to obtain the answers



  1. Missing Content - No content in the system, LLM should response like "I don't know"
  2. Missed the Top Ranked Documents - Document is exist but never ranked within the selected top k.
  3. Not in Context - Consolidation strategy Limitations
  4. Not Extracted - Answer was presented but LLM failed to extracted. Typically occurs when there is too much noise.
  5. Wrong format - the question involve extracting information from a specific format such as table.
  6. Incorrect Specificity - Not specific or too specific to address user's need, for example, on teacher/student AI use-cases, the response must include educational content along with answer, not only the answer itself. Typically occurs when users are not sure how to ask a question.
  7. Incomplete - The response from LLM is incomplete, for example when you ask "What is the key points of document A, B, and C". A better approach is to ask these questions separately.


  • Large context get better results - contrary to prior work with GPT3.5
  • Adding meta-data improves retrieval - Adding file name, and chunk number into retrieved context helped the reader extract the required information. Useful for chat dialogue
  • Open source embedding models perform better for small text
  • RAG systems require continuous calibration - due to unknown input received during runtime.
  • RAG pipeline configuration is needed - calibrating chunk size, embedding strategy, chunking strategy, retrieval strategy, consolidation strategy, context size, and prompts.

Future research directions

1. Chunking and Embeddings

There are 2 ways of chunking

  • Heuristics based (using punctuation, end of paragraph, etc.)
  • Semantic chunking (using the semantics in the text to inform start-end of a chunk) Embedding a multimedia and multimodal chunks such as table, figures, formulas may needed