From first prompt to production agents.

A calm, jargon-free path from never having used a chatbot to using AI confidently every day.

1. 01Pick one assistant and use it exclusively for a week so you learn its personality.
2. 02Replace one recurring chore — email drafts, meeting notes, trip planning — with an AI workflow.
3. 03Learn to give context: role, goal, audience, constraints, format.
4. 04Practise verification: always ask where a claim came from before you rely on it.
5. 05Build a personal prompt file of the five things you ask for most often.

The small number of prompt techniques that reliably improve output quality, minus the folklore.

1. 01State the job, not the vibe: deliverable, audience, length, tone.
2. 02Give one strong example instead of five weak instructions.
3. 03Ask for a plan before the answer on anything complex.
4. 04Request a self-critique pass and a revised final version.
5. 05Iterate on the prompt, not the output — fixes should be permanent.

How to assemble models, retrieval, orchestration and observability without over-engineering day one.

1. 01Start with one hosted frontier model and no framework.
2. 02Add retrieval only when the model demonstrably lacks the knowledge.
3. 03Introduce tracing before orchestration — you cannot debug what you cannot see.
4. 04Route cheap, high-volume tasks to smaller models once quality is proven.
5. 05Keep a written eval set from day one and run it on every change.

Workflows for image, video, voice and copy that keep a consistent house style across a whole brand.

1. 01Codify a house style preamble: palette, lighting, lens, forbidden clichés.
2. 02Generate in batches, curate ruthlessly, and grade the survivors.
3. 03Keep source prompts alongside assets so anything can be regenerated.
4. 04Use voice cloning only with documented consent and clear labelling.
5. 05Review every published asset with a human editor — no exceptions.

Chunking, hybrid retrieval, reranking and grounding checks — the pipeline that survives real documents.

1. 01Chunk on semantic boundaries, never on fixed character counts alone.
2. 02Combine keyword and vector retrieval; pure embeddings miss exact identifiers.
3. 03Rerank aggressively — precision at the top of the list matters more than recall.
4. 04Force citations and reject answers whose claims are not supported by retrieved spans.
5. 05Log retrieved context with every response so failures are reproducible.

Guardrails, budgets, retries and human-in-the-loop patterns for agents that touch real systems.

1. 01Give every agent run a hard token, time and tool-call budget.
2. 02Make destructive tools require explicit confirmation or a dry-run mode.
3. 03Design idempotent tools so a retry never doubles an action.
4. 04Checkpoint state so a failed run resumes instead of restarting.
5. 05Escalate to a human on low confidence rather than guessing loudly.