Skill Guide

LLM API integration (OpenAI, Anthropic, Cohere, open-source models)

The practice of programmatically connecting application code to multiple LLM provider APIs (e.g., OpenAI, Anthropic, Cohere) and self-hosted open-source models (e.g., via vLLM, TGI) to execute inference, manage prompts, and handle responses within a production system.

This skill enables organizations to build scalable, vendor-agnostic AI features that reduce cost, mitigate single-provider risk, and leverage the best model for each task. Directly impacts time-to-market for AI-powered products and controls operational expenditure on inference.

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8.7 Avg Demand

25% Avg AI Risk

How to Learn LLM API integration (OpenAI, Anthropic, Cohere, open-source models)

1. Understand REST API fundamentals: HTTP methods, authentication (API keys), JSON request/response bodies. 2. Learn the core concepts of a single provider's SDK (e.g., OpenAI's Python library): message formatting, model selection, and basic error handling. 3. Master prompt engineering basics: crafting clear system/user messages to get reliable model output.

1. Implement a provider abstraction layer: Design a unified interface that translates internal calls to different provider SDKs (OpenAI, Anthropic, Cohere). Handle their differing message schemas (e.g., Anthropic's system parameter). 2. Build production resilience: Implement exponential backoff, rate limiting, timeouts, and circuit breakers. 3. Integrate observability: Use logging and tracing (e.g., OpenTelemetry) to monitor latency, token usage, and cost per request. A common mistake is hardcoding provider logic, making future changes brittle.

1. Architect a model routing system: Design logic to automatically select the optimal model (e.g., GPT-4 for complex reasoning, a fine-tuned Cohere Command-R for RAG, a local Mistral for high-volume, low-cost tasks) based on query complexity, latency requirements, and cost budget. 2. Master infrastructure for open-source models: Orchestrate model serving with Kubernetes using tools like vLLM or TGI, managing GPU resources and autoscaling. 3. Implement advanced evaluation and fine-tuning pipelines: Use frameworks like Evals or LMQL to systematically test and improve integration quality, and manage fine-tuning jobs across providers.

Practice Projects

Beginner

Project

Build a CLI Q&A Bot with Provider Fallback

Scenario

Create a command-line interface tool that answers user questions. It should first try OpenAI's API; if it fails due to rate limits or errors, it should automatically fall back to Anthropic's API.

How to Execute

1. Set up a Python project with `openai` and `anthropic` SDKs. 2. Write a main function that prompts for input and calls an OpenAI endpoint using `client.chat.completions.create`. 3. Wrap the call in a try/except block. In the except block for `openai.RateLimitError`, instantiate the Anthropic client and make an equivalent call to `client.messages.create`. 4. Test by temporarily using an invalid OpenAI key to trigger the fallback.

Intermediate

Project

Develop a Unified LLM Service with Cost Tracking

Scenario

Create an internal Python service (e.g., using FastAPI) that exposes a `/generate` endpoint. It must accept a prompt and a `provider` parameter (`openai`, `anthropic`, `cohere`, `local`). For 'local', it calls a locally running vLLM server. The service must log the token count and estimated cost for every request.

How to Execute

1. Define a Pydantic model for the request. 2. Implement a `ProviderRouter` class with methods like `generate_openai(prompt)`, `generate_anthropic(prompt)`, etc. 3. For the 'local' provider, use the `openai` Python client pointed at `http://localhost:8000/v1` (vLLM's OpenAI-compatible endpoint). 4. Use middleware or a decorator to intercept each request, parse usage from the response (e.g., `response.usage`), calculate cost based on known per-token prices, and log it to a database or file.

Advanced

Project

Implement an Intelligent Query Router for a RAG Pipeline

Scenario

Design a system for a customer support bot that uses a vector database. It must classify the user's query to route it: simple FAQs go to a fast, cheap model (Cohere Command-R), complex troubleshooting goes to a powerful model (GPT-4), and queries requiring proprietary knowledge get routed to a fine-tuned local Llama 3 model.

How to Execute

1. Build a lightweight classifier (could be a smaller LLM call itself or a fine-tuned BERT model) to label queries as 'simple', 'complex', or 'proprietary'. 2. Define the router logic: `simple` -> Cohere, `complex` -> OpenAI (with retrieval augmentation), `proprietary` -> local fine-tuned model. 3. For the RAG path, implement a retrieval step before calling GPT-4, injecting the context into the prompt. 4. Implement A/B testing capability to compare model performance on the same query class, and log metrics (accuracy, cost, latency) to inform future routing rules.

Tools & Frameworks

SDKs & Client Libraries

openai (Python/Node)anthropic (Python/Node)cohere (Python)transformers (Hugging Face)langchainllamaindex

Core SDKs for direct provider interaction. `openai` SDK is also compatible with vLLM/TGI endpoints. LangChain/LlamaIndex provide higher-level abstractions and chain orchestration, but add complexity-evaluate if raw SDKs suffice for your use case.

Infrastructure & Serving

vLLMTGI (Text Generation Inference)NVIDIA TritonAWS SageMaker InferenceCloudflare Workers AI

For self-hosting open-source models. vLLM and TGI are industry standards for high-throughput serving with PagedAttention. Use cloud inference services for managed, scalable GPU endpoints.

Observability & Evaluation

LangSmithWeights & Biases PromptsOpenTelemetryPhoenix (Arize)Evals (OpenAI)LMQL

LangSmith and W&B Prompts provide tracing, logging, and playgrounds for LLM apps. OpenTelemetry for standardized distributed tracing. Phoenix for open-source observability. Evals and LMQL for structured evaluation and constrained generation.

Deployment & Routing

PortkeyLiteLLMAI Gateway (Cloudflare)

Portkey and LiteLLM are proxy servers that provide a unified API for 100+ LLMs, handling load balancing, fallbacks, and cost tracking. Critical for multi-provider production systems.

Interview Questions

Answer Strategy

The interviewer is assessing system design skills and foresight. Use the Strategy or Adapter design pattern. Outline defining a common `LLMProvider` interface/abstract class with a `generate(prompt, **kwargs)` method. Each provider (OpenAIAdapter, AnthropicAdapter) implements this interface, handling its own SDK specifics and error mapping. The core application code only programs to the interface. Mention dependency injection for swapping implementations. For API updates, changes are isolated to the specific adapter class. Sample: 'I'd implement the Adapter pattern. We define a standardized `generate` method contract. Each provider-specific adapter translates our internal request format to its API and maps its response back. This isolates us from vendor changes; if OpenAI deprecates a parameter, we only update the OpenAIAdapter, leaving the core business logic untouched.'

Answer Strategy

Tests debugging methodology and production mindset. Structure your answer: 1. Triage (is it latency, cost, or both?), 2. Isolate (provider, model, or our code?), 3. Diagnose (use logs/traces), 4. Implement fixes. Sample: 'First, I'd check our observability dashboards for error rates and latency percentiles (p95, p99) broken down by provider and model. If latency spikes correlate with a specific provider, I'd check their status page. If it's our code, I'd trace a slow request to see if time is spent in the API call or our pre/post-processing. For cost, I'd analyze token usage logs to see if a recent prompt change inflated context length. Based on findings, I might implement stricter timeouts, adjust the prompt template, or route non-urgent traffic to a cheaper model during peak hours.'