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Rolled out AI in your QA processes, but something's off?

• The agent runs slow.
• Results are inconsistent, depending on the model's "mood."
• Burns through tokens — with little to show for it.
• Prompts longer than "War and Peace."

Sound familiar? Let's fix it.

I'll share my hands-on experience building an efficient context-retrieval system for QA, using one of the most challenging domains — information security — as a case study.

We'll break down each context source and how to work with it individually, then assemble them into a single system — fast, consistently high-quality, and token-efficient — robust enough to support QA agents at every stage of their work.