You've shipped a RAG system. It works... mostly. Sometimes the answers are impressively good. Sometimes it confidently states something that's completely wrong, citing a document that doesn't say what the model claims it says. And sometimes it misses the answer entirely even though the right document is sitting right there in your knowledge base.
This is the normal state of a production RAG system. The question isn't whether you have quality issues — you do — it's whether you have a systematic way to find and fix them. That's what RAG retrospectives are for: regularly examining where your pipeline breaks down and making targeted improvements instead of guessing.
Why RAG Systems Need Their Own Retrospectives
RAG isn't one system. It's a chain of components, and the quality of each link determines the final output. When the answer is bad, the failure could be anywhere:
- Ingestion: Documents were parsed incorrectly, chunks were split in bad places, metadata was lost
- Retrieval: The search query didn't match the right documents, the embedding model missed the semantic connection, your top-K was too small or too large
- Context assembly: Retrieved chunks were relevant individually but contradicted each other, or the context window was stuffed with noise
- Generation: The model hallucinated despite good context, or it ignored relevant context in favor of its parametric knowledge
Standard software retrospectives aren't equipped to untangle these failure modes. You need a format that traces bad outputs back through the pipeline to find the actual point of failure. Otherwise you end up "fixing" retrieval when the real problem was chunking, or rewriting prompts when the real problem was retrieval.
Metrics Worth Tracking
Before running a RAG retrospective, you need data. Not all possible metrics — just enough to diagnose the most common failure modes.
Retrieval Quality
Precision@K: Of the K documents retrieved, how many were actually relevant? If you're pulling back 10 chunks and only 2 are useful, you're flooding the context window with noise.
Recall@K: Of all the relevant documents in your knowledge base, how many ended up in your top-K results? Low recall means the right answers exist but your retrieval can't find them.
MRR (Mean Reciprocal Rank): Where does the first relevant result appear in your ranking? If the best document is consistently at position 5 instead of position 1, your ranking needs work even if recall is fine.
You don't need to compute these across your entire knowledge base. Sample 50-100 recent queries, have a human judge which retrieved documents were relevant, and calculate from there. Do this monthly.
Generation Quality
Faithfulness: Does the generated answer actually reflect what the retrieved documents say? This is the hallucination question. You can spot-check this by comparing outputs against the context that was provided.
Answer relevance: Does the response actually answer the question that was asked? It's possible to generate a perfectly faithful summary of the retrieved documents that completely misses the user's intent.
Context utilization: When the right information is in the retrieved context, does the model actually use it? If you're consistently retrieving good documents and the model is ignoring them, that's a generation-side problem (usually a prompting issue).
Operational Metrics
Latency: How long does the full pipeline take from query to response? Break this down by component so you know if retrieval or generation is the bottleneck.
Cost per query: Track token usage and API costs. Some quality improvements (like expanding the context window or re-ranking) increase cost significantly.
Running the Retrospective
Preparation (Before the Meeting)
Assign someone to prepare a "failure sample" — 10-15 recent queries where the output was wrong or poor quality. For each one, capture the full pipeline state: the original query, what was retrieved, what context was sent to the model, and what the model generated. This trace is essential. Without it, you're debugging blind.
Also prepare your metric trends. Are things getting better or worse since the last retro? Any sharp changes?
The Meeting (60 minutes)
Metric review (10 minutes). Walk through the retrieval and generation metrics. Focus on trends and surprises, not a number-by-number recitation. "Precision@5 dropped from 0.72 to 0.58 this month" is useful. Reading every metric off a dashboard is not.
Failure analysis (35 minutes). This is the core of the retro. Take the failure sample and classify each one by where the pipeline broke:
- Retrieval failure: The right documents weren't retrieved. Why? Query-document mismatch? Embedding model limitation? Metadata filtering too aggressive?
- Chunking failure: The right document was retrieved, but the chunk boundaries split the answer across two chunks and only one was returned. Or the chunk was too large and diluted with irrelevant content.
- Context failure: Good chunks were retrieved, but the context window ordering or truncation lost the important information. Or conflicting chunks confused the model.
- Generation failure: Good context was provided, but the model hallucinated anyway, ignored the context, or gave a vague answer instead of the specific one available in the documents.
For each failure, ask: "What's the cheapest fix that would have caught or prevented this?" Sometimes it's a prompt tweak. Sometimes it's re-chunking a specific document. Sometimes it's a systemic change.
Prioritization and action items (15 minutes). Group the failures by root cause. The pattern that caused the most failures gets the most attention. Pick 2-3 improvements to implement before the next retro.
Common Failure Patterns and Fixes
Here are the patterns you'll see most often and practical approaches to each:
"The right document is in our knowledge base but retrieval misses it." This is usually an embedding similarity problem. The user's query uses different vocabulary than the source document. Fixes: add a query expansion step (rewrite the user's query into multiple phrasings), implement hybrid search (combine semantic embeddings with keyword matching like BM25), or improve your metadata filtering to narrow the search space.
"We retrieve the right document but the wrong chunk." Your chunking strategy matters more than most teams realize. If you're using fixed-size chunking (e.g., 500 tokens), you're almost certainly splitting important content across boundaries. Fixes: use semantic chunking (split based on topic shifts), add chunk overlap, try hierarchical chunking where larger parent chunks provide context for smaller child chunks.
"The model ignores good context and makes things up." This is a prompting and model behavior issue. The model's parametric knowledge is conflicting with the provided context, and the parametric knowledge is winning. Fixes: adjust your system prompt to explicitly instruct the model to only use provided context, add a "if the context doesn't contain the answer, say so" instruction, consider reducing the model's temperature.
"Answers are correct but too slow." Latency problems usually come from one of three places: too many retrieval calls, too large a context window (more tokens = slower generation), or re-ranking steps that add processing time. Profile your pipeline component by component. The fix depends on where the time is going.
"Quality is inconsistent — great for some topics, terrible for others." This usually means some parts of your knowledge base are better indexed than others. Maybe certain documents were parsed poorly, or certain topics lack sufficient coverage. Map your failures by topic area and you'll find the gaps.
Building a Continuous Improvement Loop
The most effective RAG teams treat their system like a product, not a project. It's never "done." Every retrospective should produce incremental improvements, and those improvements should be measurable at the next retro.
A practical cadence:
- Weekly: Quick review of automated quality metrics (can be async, just check the dashboard)
- Bi-weekly or monthly: Full retrospective with failure analysis
- Quarterly: Bigger architectural decisions — should we change embedding models, restructure our knowledge base, adopt a new chunking strategy?
Keep a running document of what you've tried and what impact it had. RAG optimization is iterative and nonlinear — you'll sometimes revisit approaches that didn't work before because the rest of the pipeline has changed enough that they work now.
Avoid the Shiny Object Trap
Every week there's a new paper or framework claiming to solve RAG quality. Resist the urge to rearchitect your pipeline based on a blog post. Instead, use your retrospective data to identify your actual biggest quality problem, and solve that specific problem. Maybe the answer is a fancy new re-ranking model. More likely, it's fixing how you chunk your product documentation.
The teams that improve fastest aren't the ones using the most sophisticated architecture. They're the ones with the tightest feedback loop between "this output was bad" and "here's specifically why, and here's what we changed."
Try NextRetro free — Categorize RAG failure patterns with columns and vote on which pipeline improvements to prioritize.
Last Updated: February 2026
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