Claude API
Build · Test
Build model-backed features with messages, tool use and responses you can evaluate.
All capabilities · LLM application development
Integrate LLM APIs into an application
Call OpenAI/Anthropic/Gemini/Bedrock APIs with streaming, retries, token accounting and cost awareness.
Explore 4 tools for this projectMarket relevance
Which roles ask for this — and how often
Share of job postings in India, per role, that name this capability.
What employers mean
You should be able to…
- Call OpenAI/Claude/Gemini APIs from a backend service with proper error handling
- Implement streaming responses so the UI shows tokens as they arrive
- Add retry/backoff logic for rate limits (429) and transient 5xx errors
- Track token usage and cost per request/user for billing or budgeting
- Swap between model providers (OpenAI, Anthropic, Bedrock, Vertex) behind a common interface
- Handle API keys/secrets securely (env vars, secret managers) not hardcoded
- Set sane timeouts and max_tokens to avoid hung requests in production
Needs first: Write production-quality Python for AI work
Learn — free, link-checked
The few resources that matter
Read · beginner · 15 min · docs.anthropic.com
Token Counting
Shows how to count tokens before sending a request so you can budget cost and avoid context overflows. — Anthropic
Read · beginner · 25 min · docs.anthropic.com
Anthropic API — Getting Started
Fastest correct path to your first authenticated Claude API call, streaming, and error handling patterns. — Anthropic
Read · beginner · 25 min · platform.openai.com
OpenAI API Quickstart
Same task as the Anthropic quickstart on the other major provider — you need both to build a provider-agnostic wrapper. — OpenAI
Watch · beginner · 60 min · youtube.com
[1hr Talk] Intro to Large Language Models
Best single hour to actually understand what tokens, context windows and cost are before you start optimizing them. — Andrej Karpathy
Read · intermediate · 35 min · docs.anthropic.com
Tool Use (Function Calling) with Claude
Canonical reference for defining tool schemas and getting reliable structured calls back from Claude. — Anthropic
Tools for practice
Choose a tool for the job
Start with one tool for each part of your project. You don’t need to learn them all.
4 tools to explore
OpenAI API
Build · Test
Connect model calls, tool use and structured responses to your own application.
Amazon Bedrock
Build · Deploy
Connect managed models to an application and explore the surrounding AWS controls.
tiktoken
Build
Count tokens and compare how prompt changes affect the input budget.
Practice
Multi-provider LLM chat CLI with cost tracking
Build a command-line chat tool that talks to two different LLM providers behind one shared interface, streams the response token-by-token, and logs the cost of every call in INR (fixed USD-INR rate) to a local SQLite file. Any two providers work: if you don't already hold OpenAI or Anthropic credit, Google AI Studio and Groq both issue a key with no card, so the whole build runs on zero spend. Add retry-with-backoff for rate limits and a --budget flag that ends the session once cumulative spend crosses a limit.
Start from
Milestones
- Get a streaming call working against one provider from the CLI · ~2.5h
- Put both providers behind one interface selected by --provider · ~2.5h
- Log tokens and INR cost per call to SQLite and add --report · ~2.5h
- Add backoff on 429/5xx and wire up the --budget cutoff · ~2.5h
Done when
- Switching --provider anthropic|openai changes the backend with no other code changes
- Responses stream to stdout token-by-token, not all at once
- A 429/5xx from the API triggers exponential backoff and retries, visible in logs
- Every call's token usage and estimated INR cost is persisted and summable via a --report command
Prove it
Evidence a recruiter can check
- A --report table pasted in the README showing token counts and INR cost across 20+ real calls
- A latency and spend comparison of both providers on the identical 20-prompt set, with the numbers rather than claims
- A log excerpt from a forced 429 showing the backoff intervals and the retry that succeeded
- Unit tests that mock the provider client and assert retry behaviour on 429/500 without spending tokens
Signal it
Built a multi-provider LLM chat CLI with streaming, retry-with-backoff and per-call INR cost tracking - benchmarked latency and spend across two providers on an identical prompt set, with a budget cap that ends a session before it overruns.
Interview
Questions you'll get asked
- How do you handle a 429 rate-limit error from the OpenAI/Claude API in production?
- Walk me through how you'd stream a response from an LLM API to a React frontend.
- How do you estimate and control the cost of an LLM feature before shipping it?
- What's the difference between synchronous and streaming API calls, and when do you use each?
- How would you design a wrapper that lets you switch from GPT-4o to Claude without rewriting business logic?
- How do you test code that calls an external LLM API without burning tokens on every CI run?