45 practical skills to get better results from any AI.
Match the task to the model — the most powerful isn't always the right choice.
A decision framework for when to invest in fine-tuning versus solving the problem with better prompts.
Protect your AI system from users (or documents) trying to hijack its instructions.
The maximum amount of text an AI model can process in a single conversation.
The hidden instruction layer that shapes how an AI responds before you say a word.
Training a model further on your own data to bake in style, domain knowledge, or behaviour.
Retrieval-Augmented Generation: give the AI access to your own data at query time.
Temperature controls how random or predictable an AI's responses are.
How AI models break text into chunks called tokens — and why it matters for cost and limits.
Proven workflows for using AI to write, review, debug, and document code faster.
Unlock Claude's internal reasoning mode for hard problems that benefit from longer deliberation.
Analyse datasets, generate hypotheses, and produce visualisation code using natural language.
Use AI to accelerate literature review, synthesis, and insight extraction across large document sets.
Extract information, analyse images, and build multimodal workflows with GPT-4o's vision capabilities.
Structure, draft, and refine long-form content — articles, reports, scripts — using AI as a collaborator.
Cut your LLM API bill by 40–80% without sacrificing output quality.
Use async batch APIs for non-real-time workloads — up to 50% cost savings.
Automatically send each task to the cheapest model capable of handling it well.
Cache repeated prompt prefixes to save tokens and reduce latency on every call.
Fit more task into fewer tokens using compression techniques that preserve semantic meaning.
Stay under context limits on long conversations without losing critical information.
Layer expertise, personality, constraints, and goals into a rich persona for highly tailored output.
Ask the AI to show its reasoning step by step — dramatically improves accuracy on complex tasks.
Advanced CoT techniques: zero-shot CoT, self-generated rationales, and structured reasoning chains.
Have the AI critique and revise its own output against a set of principles — self-improving generation.
Tell the AI exactly how to structure its response — bullet points, JSON, tables, markdown.
Give the AI 2–5 worked examples to establish the pattern you want it to follow.
Treat prompting as a conversation — refine and build on each response.
Use headers, bullets, and code blocks in your prompts to structure complex instructions clearly.
Use the AI to generate, critique, and improve prompts — prompts that write prompts.
Learn when a longer prompt helps and when it hurts — and how to trim without losing intent.
Practical techniques to stop AI from confidently making things up.
Assign the AI a persona or role to shape its tone, expertise, and style.
Sample multiple answers to the same question and pick by majority vote — boosts accuracy without fine-tuning.
Reliably extract JSON, XML, or typed schemas from language models for use in code.
Design system prompts like software — with sections, constraints, examples, and fallback behaviour.
Have the AI explore multiple reasoning branches simultaneously and evaluate the best path.
Precise, specific instructions produce dramatically better outputs than vague ones.
Ask the AI to complete a task with no examples — just clear instructions.
End-to-end guide: chunk documents, embed, store in a vector DB, retrieve, and generate answers.
Systematically measure output quality so you can improve prompts with confidence, not guesswork.
Build reliable AI agents that plan, act, observe, and iterate without getting stuck.
Connect AI to real-world tools — APIs, calculators, databases — via structured function definitions.
Coordinate multiple specialised AI agents working in parallel or sequence to tackle large tasks.
Break complex tasks into a pipeline of focused prompts, feeding each output as the next input.