Diagnose and fix common Retrieval-Augmented Generation breakdowns.
Even the best large language models can fall short if your RAG system isn’t built right. This resource walks through the most common failure points and how to fix them with better retrieval, ranking, and generation strategies.
Many RAG systems fail silently—retrieving the wrong documents, missing key context, or generating incomplete responses. These issues often go unnoticed until your outputs are unreliable, inconsistent, or misleading.
When RAG systems hallucinate or miss key context, users may act on incorrect answers, creating downstream risk in critical workflows like customer support, legal research, or internal knowledge access.
Inconsistent or vague responses make users second-guess the system. Once trust is lost, it’s hard to rebuild, especially in customer-facing or high-stakes use cases.
If retrieval and ranking aren’t optimized, your GenAI stack becomes expensive noise. Valuable engineering time goes into patching prompt issues or chasing down hallucinated output.
Inaccurate, unstructured, or incomplete responses can stall RAG deployments entirely. Teams can’t move forward until the system produces answers that are consistent, traceable, and usable.
Most RAG failures start before generation even begins. Learn how to spot low-quality retrieval, ranking errors, and poor consolidation strategies before they undermine your AI’s performance.
Learn how to trace inaccurate outputs back to the correct source, whether it’s retrieval or generation, so you can apply focused fixes instead of guessing or over-engineering prompts.
Fine-tune your retrievers and rerankers to ensure the most useful context is included in the LLM’s input. Better ranking leads to more complete, accurate, and grounded answers.
Guide your LLMs to return usable outputs by enforcing structured formats. This reduces downstream cleanup and increases answer consistency across use cases.
Use query rewriting and prompt adjustments to clarify user intent before retrieval begins. Cleaner queries lead to better document matches and more precise responses.
"We minimized labeling drift and boosted overall model performance by 30%. These steps helped us avoid a regulatory setback that could’ve delayed our entire pilot."
Director of Data Science
Global Tech & Healthcare Company
If you’re wrestling with complex labeling challenges, compliance audits, or repeated model drift, our experts can diagnose your data pipeline and suggest a tailored approach.