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Core Concepts
AI and LLM basics — what AI can do, classical ML literacy, tokens, attention, embeddings, prompting, RAG, agents, evals, multimodal, open vs closed models, and privacy.
How this relates Browse here for Hello Interview–style articles. The guided path below in the sidebar remains the project milestone sequence.
Articles in this track
- What is AI (and what it is not)Machine learning vs deep learning vs generative AI — vocabulary, limits, and when not to use an LLM.Open
- Classical ML literacyTrain/val/test splits, leakage, overfitting, and why baselines still matter in an LLM world.Open
- TokenizationTokens are the interface between UTF-8 text and model IDs — BPE, costs, chat templates, and why bad splits break products.Open
- Self-attentionAttention lets every token look at every other token — Q/K/V, multi-head, causal masks, and why context length is expensive.Open
- Embeddings and similarityVectors that put “nearby meaning” nearby in space — foundation of semantic search, RAG, and clustering.Open
- Prompt engineering fundamentalsPrinciples from DeepLearning.AI’s prompt course — clear instructions, delimiters, iterative refinement, and when to stop prompting and build systems.Open
- Structured outputsJSON schemas, tool calls, and validation loops — making LLMs produce machine-checkable results.Open
- RAG building blocksChunking, hybrid retrieval, rerank, citations, and evals — the grounded-generation system before frameworks.Open
- Agents and the ReAct loopAn agent is an LLM in a loop over tools and state — what it is, how products use it, architecture, how to build one, today’s stacks, and failure modes.Open
- Evals fundamentalsOffline golden sets, online monitors, component vs E2E, judges, and ship gates before demos.Open
- Serving and streamingFrom generate() to production APIs — TTFT, tokens/sec, SSE streaming, batching, and OpenAI-compatible façades.Open
- Networking for AI appsTimeouts, retries, idempotency, websockets vs SSE, and how LLM latency changes API design.Open
- LLM project lifecycleScope → model choice → adapt (prompt/RAG/fine-tune) → evaluate → deploy → monitor — the GenAI-with-LLMs lifecycle for product teams.Open
- Multimodal basicsImages, audio, and documents into LLMs — when modality helps, how APIs expose it, and failure modes (OCR, resolution, cost).Open
- Privacy and data for AI appsPII, retention, tenant isolation, and what not to put in prompts, logs, or vector indexes — the AI-for-Everyone society week applied to builders.Open
- Open-weight models vs closed APIsWhen to call OpenAI/Anthropic vs run Llama/Mistral yourself — cost, latency, data control, and capability tradeoffs.Open