Stage 3 of 9
LLM Capabilities for Product Decisions
Turn model strengths, latency, cost and context limits into product decisions you can defend.
Level: Intermediate About 2 weeks at 6 hours a week 6 articles
By the end of this stage you can
- Estimate latency and cost per task before you build
- Choose a model with a repeatable decision framework
- Decide between building, buying and fine-tuning
Articles
- 3.1 What LLMs are good and bad at A product-focused map of LLM strengths and weaknesses, from language tasks to tool use, and where models still fail, so you scope features realistically. Level: Intermediate 6 min Completed
- 3.2 Latency budgets and perceived speed Set latency budgets for AI features, measure time to first token and total time, and use streaming and UX to make responses feel fast. Level: Intermediate 9 min Completed
- 3.3 Estimating cost per task Estimate what an AI feature costs per task and per successful task, from token counts and model prices to retries and caching, with a reusable cost calculator. Level: Intermediate 9 min Completed
- 3.4 Context windows, retrieval and memory trade-offs Decide what goes into the model's context, from long context versus retrieval to conversation memory, and the cost, latency and quality trade-offs of each. Level: Intermediate 8 min Completed
- 3.5 Choosing a model: a decision framework Choose the right model for each AI feature with a repeatable framework that weighs quality on your evals, latency, cost per success, limits and effort settings. Level: Intermediate 10 min Completed
- 3.6 Build, buy or fine-tune Decide whether to build on a model API, buy an existing AI product, or fine-tune a model, with the costs, risks and evidence each option needs. Level: Intermediate 6 min Completed