Building with LLM APIs
Call language models from your own code: requests, streaming, tools and costs.
30tutors
6teachers
3free to start
$3 to $12per paid lesson
Building with LLM APIs tutors
30 tutors
Batch Jobs for Large LLM Workloads
Batch Jobs for Large LLM Workloads
Run thousands of model requests as batch jobs that are cheaper, resumable and easy to check.Farid Haddad$7LLM APIs on a Solo Developer BudgetLLM APIs on a Solo Developer Budget
Build and launch a side project on model APIs without a surprise bill, abuse or an overbuilt setup.Farid Haddad$4Context Window Budgeting for AppsContext Window Budgeting for Apps
Plan what fits in each request: system prompt, documents, history and room for the answer.Greta Lindqvist$4Multilingual Apps on LLM APIsMultilingual Apps on LLM APIs
Serve users in many languages with consistent quality, sensible cost and testing per language.Greta Lindqvist$6Temperature and Sampling Settings ExplainedTemperature and Sampling Settings Explained
Know what temperature, top_p, length limits and stop sequences really do, and which values suit a task.Gabriela Sousa$3Prompt Templates in Your CodebasePrompt Templates in Your Codebase
Store, fill, version and test prompts as proper files instead of long strings scattered through your code.Gabriela Sousa$4Teachers who teach Building with LLM APIs
They wrote the lesson plans these tutors follow.
Emeka Nwosu
Search engineer teaching embeddings, chunking, vector and keyword search, and reranking from first principlesembeddings, chunking, vector databases9 tutorsSampleFelix Brandt
Numerical Python and code quality for data and AI projects that have outgrown a single notebookNumPy, pandas, plotting9 tutorsSampleFarid Haddad
Keeps LLM features fast, affordable and available as traffic grows: cost, caching, retries and observabilitytoken counting, cost control, prompt caching9 tutorsSampleGabriela Sousa
Teaches developers and product teams to make their first LLM API calls and design simple apps around themfirst API requests, messages and roles, system prompts9 tutorsSampleGreta Lindqvist
Structured output, tool calling and safe input handling for LLM applications that must behave predictablystructured output and JSON schemas, tool and function calling, image and document input9 tutorsSampleYohannes Tesfaye
Machine learning engineer who runs portfolio reviews and mock interviews for technical AI rolesML engineering interviews, data science interviews, AI product manager interviews9 tutorsSampleMore in Building with AI
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