Common Challenges

  1. Retrieval Noise and Irrelevance

    • Retrieved chunks may contain:

      • Repeated information

      • Marginally relevant content

      • Contradictory or misleading data

    • Impact: Can confuse the generator or lead to hallucinated answers.

    • Solution: Reranking, metadata filters, and query rewriting.

  2. Query Ambiguity and Under-Specification

    • User queries may be:

      • Too vague (“Tell me about the law”)

      • Using overloaded terms (e.g., “LLM” = Large Language Model or Master of Laws)

    • Impact: Poor retrieval due to mismatches.

    • Solution: Query rewriting, sub-query decomposition, classification modules.

  3. Token and Context Limits

    • LLMs have a strict input token limit (e.g., 8k–100k tokens).

    • Long retrieved content must be:

      • Compressed

      • Summarized

      • Selected selectively

    • Impact: Truncated or low-quality input reduces answer quality.

  4. Latency and Cost

    • RAG systems involve:

      • Document retrieval

      • Vector encoding

      • LLM inference

    • Impact: Increased response time and operational costs.

    • Solution: Caching, query classification (skip retrieval when not needed), smaller models.

  5. Evaluation Complexity

    • Hard to measure performance consistently because:

      • Retrieval and generation quality are interdependent.

      • No universal benchmarks for all domains.

    • Solution: Combine automated and human-based evaluation (LLM-as-a-judge + manual review).

  6. Domain-Specific Knowledge Integration

    • RAG struggles with:

      • Highly specialized vocabularies (e.g., legal citations, scientific formulas).

      • Tables, charts, and non-textual data.

    • Impact: Loss of critical context.

    • Solution: Use structured data (e.g., SQL + RAG), convert tables to text, or train with examples.

  7. Security, Bias, and Misinformation

    • If the knowledge base is not curated:

      • RAG may surface outdated, biased, or even harmful content.

    • Impact: Erosion of user trust or factual correctness.

    • Solution: Source filtering, content audits, feedback loops.