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Explicateurs préférés
AI Cost Optimization: Cutting LLM Bills 80%
Most LLM bills can be cut by 50–90% without quality loss. Caching, model routing, prompt diet, and output caps deliver the bulk of it.
AI Observability: Tracing Every Token in Production
Without traces, every LLM bug is a guess. Capture prompts, tool calls, tokens, costs, and latencies for every request — searchable, filterable, alertable.
LLMOps: MLOps for the LLM Era
LLMOps is the operational discipline of running LLM apps in production — prompts as code, evals on every change, observability, cost, and incident response.
Choisis ton prochain concept
AI Coding Assistants Compared: Cursor vs Copilot vs Claude Code
Three philosophies — Copilot completes, Cursor edits, Claude Code drives. Pick by how much agency you want the AI to have.
GitHub Copilot vs Claude Code: Inline vs Agentic
Copilot speeds up typing. Claude Code finishes tasks. Different jobs, different tools — and increasingly, the same engineer uses both.
AI Code Review: How LLMs Catch Bugs Humans Miss
AI code review is a tireless first-pass reviewer. It misses architectural nuance but rarely misses an off-by-one. Use it as the floor, not the ceiling.
Agentic Coding: From Autocomplete to Autonomous Engineer
Agentic coding is when the AI plans, edits, runs tests, and iterates — without holding your hand at every step. The unit of work shifts from "line" to "task."
Cursor: The AI-Native Code Editor Pattern
Cursor takes VS Code and rebuilds the editor around AI — multi-file edits, tab-to-accept, repo-aware chat. The "editor as AI surface" approach.
Spec-Driven Development With AI Coding Agents
When the AI is the implementer, the spec is the lever. Tight specs ship fast and clean; vague specs ship vague code. Treat the spec like the new keyboard.
Function Calling: How LLMs Use Your APIs
You describe a function in JSON Schema; the model decides when to call it and produces typed arguments. The bridge from "pretty text" to "real action."
Structured Outputs and JSON Mode: Reliable LLM Responses
Stop parsing prose. Constrain the model's output to a schema and your downstream code stops guessing. The single biggest reliability lever in LLM apps.
LLM Streaming: Why First-Token Latency Beats Total Time
Streaming sends tokens as the model produces them. Total wall-time is similar; perceived speed is dramatically better — and lets you cut off when the answer is good enough.
API Rate Limits, RPM, and TPM Explained
Two budgets you cannot ignore: requests per minute and tokens per minute. Understanding both — and the burst behaviour around them — keeps your prod stable.
LangChain vs LlamaIndex: When to Pick Which
LangChain is the kitchen-sink agent framework. LlamaIndex is the RAG-focused data framework. Same neighbourhood, different specialities.
OpenAI SDK vs Anthropic SDK: API Patterns Compared
Same family of APIs, different shapes. Knowing the differences saves an afternoon of head-scratching when porting code between providers.
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