AI Product Development Timeline: What 10 Weeks Actually Looks Like

Week 1–2: Foundation (Non-Negotiable)

Day 1-2: Requirements and success definition. Define: what is the AI doing? What does good output look like? What is an acceptable hallucination rate? What latency is acceptable? Without these, you have no way to evaluate progress.

Day 3: Eval framework setup. Build a test dataset of 50-100 representative examples with expected outputs. Set up automated eval that runs after every model/prompt change. This is the most important infrastructure investment of the build.

Day 4-5: Baseline model. Run the simplest possible version a zero-shot prompt with the best available model (GPT-4o or Claude 3.5 Sonnet). Score against your eval. This is your baseline. Everything you build must beat this.

Week 2: Data pipeline and integration sprint. Connect your data sources. Build the retrieval layer if RAG. Integrate with your application. First end-to-end demo by Friday of week 2.

Week 3–6: Build and Evaluate

Week 3-4: Optimise retrieval. Experiment with chunking strategies, embedding models, re-ranking. Every experiment scored against your eval. Aim for 20-30% improvement over baseline.

Week 5: Prompt engineering sprint. System prompt structure, few-shot examples, output format constraints. Most teams under-invest here we spend a full week.

Week 6: Integration hardening. Handle edge cases from eval failures. Build fallback paths. Implement PII detection if required. Add latency monitoring.

Week 7–10: Production Hardening

Week 7-8: Load testing and cost modelling. Simulate 10x expected traffic. Measure cost per inference at scale. Implement caching for repeated queries (typically reduces API costs 25-40%).

Week 9: Security review. Prompt injection testing, output filtering, access control audit. Run pen test if enterprise customers are required.

Week 10: Staged rollout. 5% traffic → 25% → 100% over 2 weeks with monitoring at each stage. Define rollback criteria. Ship.

What typically slips: data quality issues discovered in week 3-4 (poor source documents degrade RAG quality significantly). Our mitigation: data quality audit in week 1.