Learning path for developers who want to build with LLMs
From your first API call to retrieval, tool use, evaluation and deployment: an ordered path for software developers moving into AI engineering.
AI Desk3 min read
Tag
#Developers
Every The Latent Daily story, tool, model and resource tagged “Developers”.
15 items
Model deprecations explained: what happens when an AI model is retired
AI providers regularly retire older models. How deprecation timelines work, what breaks when a model is shut down and how to plan migrations without surprises.
AI Desk3 min read
AI CodingGuide
How to use AI coding assistants effectively: a practical workflow
Get better results from Copilot, Cursor, Claude Code and other coding assistants: give context, work in small steps, review every change and keep tests in the loop.
AI Desk3 min read
APIs & SDKsGuide
Your first LLM API call: a step-by-step guide in Python
Set up an API key safely, send your first request to a language model in Python, understand messages, tokens and parameters, and handle errors and costs.
AI Desk3 min read
APIs & SDKsGuide
Structured outputs and function calling: getting reliable data from LLMs
How to make language models return valid JSON that matches a schema, how function calling works, and how to validate outputs so your application never breaks.
AI Desk3 min read
How retrieval-augmented generation (RAG) works, step by step
RAG lets AI answer from your documents. Each stage explained: ingestion, chunking, embeddings, retrieval, reranking, prompting and evaluation, with practical tips.
AI Desk3 min read
Vector databases explained: how they work and how to choose one
Embeddings, similarity search, indexes like HNSW and the trade-offs between pgvector, Pinecone, Weaviate, Qdrant, Milvus and Chroma for your RAG project.
AI Desk3 min read
How to build an evaluation set for an LLM application
The most important tool in AI engineering: how to collect test cases, define success criteria, score outputs automatically and by hand, and run evals on every change.
AI Desk3 min read
10 open-source AI projects every developer should know
From Hugging Face Transformers to llama.cpp, vLLM, Ollama, LangChain and ComfyUI: the open-source projects that form the backbone of today's AI development.
AI Desk3 min read
How to evaluate an open-source AI project before you depend on it
Stars aren't enough. Check maintenance, community, license, security, documentation and governance before building on an open-source AI library or model.
AI Desk3 min read
Prompt injection and LLM security: the risks every AI app must handle
Prompt injection, data leakage, insecure tool use and model supply-chain risks explained with the OWASP Top 10 for LLM applications and practical defenses.
AI Desk3 min read
Tools tagged #Developers
AI-first code editor built on VS Code, with codebase-aware chat, multi-file edits, autocomplete and agents that carry out coding tasks.
Not rated yetFreemium
AI pair programmer from GitHub that offers code completions, chat and agent features in popular editors, on GitHub.com and in the terminal.
Not rated yetFreemium