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System prompts shape **every reply** in a session. Good ones are short, testable, and explicit about limits.
Chunking makes or breaks RAG. Goal: each chunk is **small enough to retrieve** but **big enough to answer**.
Use AI as a **study partner**, not an answer key — especially for exams.
Minimal chatbot: send user text to an LLM API, show the reply. No RAG yet — that's the next step.
Treat debugging like a checklist — don't blame the model until you know which stage failed.
Streaming shows tokens as they arrive — better UX for long replies.
Let the model request actions; your code executes them safely.
Measure retrieval and answers separately — a eloquent lie is still a failure.
Use AI as a **second pair of eyes** — not a merge approver.
Minimal RAG: five markdown/text files → embeddings → question → cited answer.
Show the model **2–5 examples** of input → desired output so it copies the pattern.
Pick with **your tasks**, not leaderboard hype.
Turn messy notes into **decisions, actions, and open questions** — then verify names and dates.
Good images come from **specific prompts** — subject, style, lighting, composition.
Chain triggers and AI steps without shipping production code — still review every output.
Embeddings power RAG retrieval — pick for **language, domain, and cost**, not buzzwords.
You can't eliminate hallucinations — but you can **ground, constrain, and verify**.
Isolate project packages so AI coding projects don't break your system Python.
Ship a minimal API or chat UI — secrets on the server, logs on, scope small.
Keep long chats useful without blowing the context window — in about 6 minutes.
Keep provider keys off the client and out of Git — a 5-minute security pass.
Send a small slice of traffic to the new model or prompt before full rollout.
Compare prompt v1 vs v2 on real tasks before you ship — about 6 minutes.
Call Claude from Python with roles and a system prompt — first message in ~6 minutes.
Get an offline chat running in about 8 minutes.
Survive 429 errors without melting your budget.
One clear question, verified numbers — about 7 minutes.
Think → tool → observe in code — starter pattern in ~10 minutes.
Route 10% of traffic to the new stack — then promote or rollback.
No single pixel test catches everything — combine **visual cues, context, and verification**.
News articles oversimplify — use this to extract **what actually applies to you**.
Index many chunks efficiently — **chunk once, embed in batches, store with metadata**.
Debug quality, cost, and incidents without **leaking secrets or PII**.
Users trust answers they can **click back to source chunks**.
Five minutes to a send-ready draft you still review line by line.
Pick the right model tier with a tiny eval — not hype.
Pre-flight before customer traffic hits retrieval + chat.
Connect a tool server to your agent client in ~10 minutes.
Keyword + vector + re-rank in a minimal pipeline.
Find the broken span before tuning prompts.
No agent reaches prod without a golden task set.
Score a prompt change before opening a PR.
Stop the model from calling tools you never intended to expose.
Human review before irreversible actions.
Automate adversarial prompts in staging before users find holes.
Style and format without retraining the whole model.
Split work across an orchestrator and bounded workers.
Image + question in one chat request.
Watch latency, quality, and cost after you ship.
Find where money goes before optimizing blindly.
Citation gate before users see text.
Compare two models or prompts on live traffic safely.
1. Classify: data leak, quality regression, latency, or cost only