Critical AI Chatbot Debug & Optimize

My production-level AI chatbot has started delivering incorrect answers that appear randomly during conversations, and the issue surfaces regardless of what the user types. I need a skilled developer to track down the root cause, correct the faulty logic or model configuration, and then fine-tune the overall stack so replies arrive fast and reliably.

The work begins with a thorough audit of the current codebase, model parameters, API calls, and any middleware that could be polluting context or truncating prompts. Once the bug is isolated, I expect a clean, well-documented patch plus recommendations (or direct implementation) for re-training, prompt engineering, or intent/slot mapping improvements—whatever actually resolves the accuracy problem.

After correctness is restored, please tackle performance. Typical targets include caching strategies, batching or rate-limit handling, thread safety, and lightweight logging so we cut latency without sacrificing stability.

Deliverables
• Diagnostic report outlining the failure point and evidence
• Fixed code or configuration with inline comments and rollback instructions
• Before-and-after metrics demonstrating accuracy regained and measurable response-time gains

Acceptance criteria: zero incorrect replies in a 200-turn regression test and an average response time no slower than 800 ms under the current traffic profile.

Tools and languages are flexible as long as you work comfortably with modern NLP/LLM frameworks, Python or Node back-ends, and cloud deployment pipelines (e.g., Docker, AWS/GCP, Git). Push changes through a feature branch so I can review via pull request before merging to production.