Building a RAG system is more than just wiring a model to a knowledge base. In enterprise settings where accuracy, consistency, and reliability matter more than quick demos—the real differentiator is tuning. In this article we explore practical ways to optimize RAG systems, referencing real configurations used with AWS Bedrock, and show how this approach aligns with Meetlabs’ vision: AI systems that understand context, reduce errors, and scale under control.
At Meetlabs we work from a clear premise: a RAG that “works” is not always a RAG that is useful. Many implementations produce an answer, but not necessarily the correct, expected, or business-useful one. As RAG systems move from internal tests to real-world use sales, support, internal analytics, or decision-making common problems appear: inconsistent answers, irrelevant information, context loss, or even hallucinations. The cause is rarely the base model. Almost always it’s lack of tuning.
An untuned RAG typically fails in very specific ways:
In business contexts, this is not just a technical issue: it’s a trust issue.
One of the first critical adjustments is how many passages are retrieved from the knowledge base but more is not always better.
At Meetlabs, tuning this parameter is key so the AI prioritizes truly actionable information.
Semantic search understands intent, but it doesn’t always capture exact terms. Hybrid search combines intent + keywords.
This is especially useful when:
The prompt that connects retrieval to generation defines the RAG’s “behavior.”
A good prompt:
At Meetlabs, this is crucial to keep coherence across different flows and teams.
Temperature, token limits, and top-p are not minor details.
Proper tuning here determines whether the system feels reliable or unpredictable.
Optimizing a RAG system is not optional it’s mandatory when building real enterprise solutions. The value is not in connecting more models, but in understanding how to retrieve, prioritize, and generate information in a controlled way. At Meetlabs, this approach moves systems from “interesting” assistants to reliable, scalable AI systems aligned with business decisions.
