How to summarize a long document
Get a useful summary without losing the thread — in about 5 minutes.
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W3Schools-style tasks — search or filter by time, then copy the steps.
74 recipes
Get a useful summary without losing the thread — in about 5 minutes.
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.
Treat citations as claims to open — not as proof.
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 + generation. Treat index and model versions as a pair.
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.
Agents should only call tools you explicitly enable — with argument validation.
Human review before irreversible actions.
Automate adversarial prompts in staging before users find holes.
Teach style and format without retraining the full base model — then prove it beats prompting/RAG on your golden tasks.
Split work across an orchestrator and bounded workers.
Send an image + question in one request, then verify before any side effect.
Watch latency, quality, and cost after you ship.
Find where money goes before optimizing blindly.
Compare two models or prompts on live traffic safely.
Put numeric columns on a similar scale so one giant feature does not dominate training.
Turn a table of predictions into actionable precision, recall, and error patterns.
Separate outage from silent quality failure — both are incidents.
Match the loss to your output type and what "wrong" means for your product.
Wire documents → embeddings → retriever → LLM with LangChain's composable chain pattern.
Train and evaluate a classic ML model in a few lines — great baseline before reaching for an LLM.
Store embeddings in a managed vector index and query by semantic similarity.
Combine Bedrock embeddings, a vector store, and `InvokeModel` for a private-cloud RAG loop.
Generate text embeddings on Google Cloud for search, RAG, or clustering pipelines.
Call your Azure deployment with the same messages JSON as OpenAI — different URL and auth.
Use the model to draft — you prove it on known rows before fill-down.
Pull owners and due dates only when stated — mark everything else TBD.
Lock audience, voice, and must-keep facts before you ask for a rewrite.
Get structure first; decorate slides only after the story and facts hold.
Bound the job before the assistant edits files.
Ground the draft in allowed macros/policy; escalate when authority is missing.
Insights are hypotheses until you recompute the critical numbers.
Feed approved facts; forbid testimonials and stats not in the pack.
Ground every question in the notes you provide; keep an answer key you verify.
Separate story, visuals, and rights — especially for synthetic media.
Keep the facts; adjust tone for audience without inventing commitments.