LLM APIs Explained for Product Teams
Understand what your engineers mean by tokens, context, latency and rate limits, and ask better questions.
A taste of a lesson
Engineering says our summary feature costs 'about 3,000 tokens a request'. What does that mean for budget?
It means each summary sends and receives about 3,000 word pieces in total. Ask them to split it into input tokens (the document plus instructions) and output tokens (the summary), because output usually costs more per token. Then the maths is simple: input tokens times input price, plus output tokens times output price, times expected summaries per month. Use the provider's current price list. Try it with a guess of 2,600 input and 400 output: what do you need to know next?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain what happens in a model API call in plain terms
- Estimate a feature's monthly cost from usage assumptions
- Describe how context limits, latency and variation affect product design
- Ask engineering for the numbers that matter before launch
- Raise data handling and model change questions early
Lesson plan
- 1 What happens in one API call Describe the request and response of a model call without code. Start
- 2 Tokens and cost Estimate a feature's cost from tokens per request and request volume. Start
- 3 Context, speed and variation Understand three constraints that shape the user experience. Start
- 4 When answers go wrong Plan the product response to hallucinations, refusals and failures. Start
- 5 Launch questions and ongoing change Prepare the questions that prevent surprises at and after launch. Start
Try asking
About this tutor
For product managers, designers, analysts and team leads who work on features built on model APIs but do not write the code. Without programming, you learn what happens in a single API call, why costs are measured in tokens, what a context window limits, why answers vary and sometimes invent facts, what drives speed, and what rate limits and data handling terms mean for a launch. Each lesson ends with questions you can bring to your engineering team and a rough estimate you can do yourself, such as monthly cost for a feature. Engineers who need to explain these ideas to colleagues will also find it useful.
Reviews
4.7
3 ratingsSample
- Charlotte D.Sample
I finally follow standups about tokens and context windows. The cost estimate exercise let me challenge a budget number with real questions instead of nodding.
- Ibrahim S.Sample
No code, clear analogies, then the proper terms. The list of launch questions went straight into our feature review template.
- Freya J.Sample
As a designer I learned why answers vary and why one demo proves nothing. Changed how I plan user testing for AI features.
About the teacher
Teaches developers and product teams to make their first LLM API calls and design simple apps around them
9 tutors 337 lessons taught Sample
I help people go from having used a chatbot to having an app that calls a model. I built web products for a long time and moved into LLM features when they started appearing in every roadmap, so my lessons focus on the decisions that matter in a first build: how a request is shaped, how a conversation is stored,...
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