How we build your AI chatbot — from blank slate to live
No sprint handoffs, no guesswork. A structured mentorship path where every phase has a clear purpose and a concrete output you can actually use.
The process
Four phases, one continuous thread
Scoping the problem space
Before writing a single line of code, we work through what your chatbot actually needs to do. Use cases, constraints, data sources, tone — all mapped out in structured sessions using tools like Miro and Notion.
Deliverable: requirements doc + conversation flow mapArchitecture and model selection
GPT-4o, Claude, Mistral, or a fine-tuned open-source model — choosing the right one depends on your latency needs, data sensitivity, and budget. We go through the tradeoffs together with real benchmarks, not marketing sheets.
Deliverable: architecture diagram + model rationalePrototype and iteration loop
We build a working prototype — usually within LangChain, Rasa, or a direct API stack — and run it against real user input. You review outputs, flag failures, and we rework the prompt logic or retrieval pipeline together until it holds.
Deliverable: testable prototype + evaluation reportDeployment and ongoing calibration
Shipping to production is phase four, not the finish line. We cover containerisation, API integration, monitoring setup, and the feedback loops that keep a chatbot useful after launch rather than degrading quietly over time.
Deliverable: live deployment + monitoring playbookWhat you leave with
Each phase ends with a real artifact you own — not slides, not theory. By the end of the mentorship you have a production chatbot, the skills to maintain it, and enough domain knowledge to make smart architectural decisions independently.
Working chatbot
Deployed, connected, and responding to real user input in your chosen environment.
Evaluation methodology
A repeatable process for testing response quality, catching regressions, and measuring improvement.
Prompt engineering depth
Practical fluency in system prompts, few-shot examples, chain-of-thought structuring, and output formatting.
Architecture decision log
Documented rationale for every major technical choice — so future you (or your team) understands the why.
Petra Vondráčková
Lead mentor, conversational AI
"The first prototype rarely works perfectly — that's the point. You learn more from debugging a broken response than from watching a demo."