Edvantis AI Webinar Insights: Why AI Coding Tools Drop to 20% on Real Codebases
Rzeszów, Poland — April 22, 2025 — Edvantis hosted an online webinar “Your Engineers Use AI Daily. Here’s How to Get More from It.”.
The session was led by Sergii Shelpuk, AI Consulting Expert with 15+ years of experience helping global enterprises adopt AI at scale. The webinar covered why AI coding tools that solve 70%+ of tasks in demos often drop to 20-30% on real commercial codebases — and what engineering teams can do to close that gap. Here are some of the key takeaways.
AI coding tools fail in predictable ways, not random ones.
Most failures with tools like Claude Code, Codex, Copilot, and Cursor trace back to how large language models are trained, not to bugs in the tools themselves. Sergii walked through four specific problems: data bias (models underperform on niche or proprietary technologies), the “bad token” problem (once an LLM produces a flawed output, it doubles down to justify it), the “always answer” problem (LLMs trained on question-answer pairs never ask for clarification), and context fragmentation across teams. Each has a known cause and a known fix — but only if the team understands what they’re solving for.
Context beats intelligence — every time.
The “intelligence” of an LLM is mostly smoke and mirrors. What these tools actually do well is process the context you feed them. As Sergii put it during the session: stop relying on what the model “knows” — supply context instead, through web search, codebase indexing, and persistent memory layers shared across the team. The pattern across high-performing teams is consistent: they treat AI coding tools as systems they build, not products they install. A poll during the webinar confirmed this — 78% of attendees said reworking how they feed context to AI tools was their top priority going forward.
Configuration is the difference between 20% and 70%.
Out-of-the-box AI coding tools deliver average results. The teams getting real productivity gains have invested in configuration: search-by-default behavior, second-LLM review, explicit clarification prompts, and structured workflows for recurring tasks. Edvantis’s own internal setup — combining indexing, smart search, second-LLM review, and a test-driven development skill — improved Claude Code output quality by 20% on real production codebases. The shift from IDE-tied to CLI tools also matters: it removes vendor lock-in and lets engineering teams run multiple agents in parallel with whatever IDE they already use.
78% of webinar attendees said reworking how they feed context to AI tools is their top priority after the session.
Watch the Webinar Recording
The presentation materials are also available on the Your Engineers Use AI Daily webinar page.
About Edvantis
Edvantis is a global software engineering and consulting company with over 20 years of experience and 400+ technology professionals. With deep expertise in AI, data engineering, and system integrations, Edvantis helps businesses build reliable technology solutions that drive efficiency and long-term growth
