Intelligence Prompting

Reviewed topic hub

Artificial intelligence prompt engineering: frameworks, guides, testing, and reusable systems

Artificial intelligence prompt engineering is the practice of designing, testing, and maintaining instructions and context for a defined AI-assisted task. Professional prompting is closer to writing a working specification than finding magic words: strong prompts define the job, provide authoritative context, handle uncertainty, require usable output, and include a repeatable evaluation method.

  • Task and output specification
  • Context engineering and source hierarchy
  • Examples, constraints, and structured output
  • Testing, versioning, and prompt operations

Learning path

Build understanding in the right order.

Start with the system model, then move into design, evaluation, and production controls. Each article links concepts to decisions.

01

24 min read

Loop Engineering vs Prompt Engineering: A Practical 2026 Guide

Learn how loop engineering turns strong prompts into reliable AI agent workflows with tools, evaluation, stop conditions, budgets, and human approval.

  • Prompt engineering remains essential: it defines the goal, context, constraints, tools, and output contract for each model decision.
  • Loop engineering adds orchestration around those prompts: state, tool calls, evaluation, retries, budgets, approval gates, observability, and explicit exit conditions.
  • The safest starting point is usually one agent in a small bounded loop, not an unrestricted multi-agent system. Expand autonomy only after traces and repeatable evaluations show that the simpler design is insufficient.
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02

22 min read

How to Improve AI Prompts: A 7-Step Method with 15 Before-and-After Examples

Improve weak AI prompts with a practical seven-step method, a diagnostic checklist, testing guidance, and 15 before-and-after examples for real work.

  • Improve prompts from observed failures instead of adding random detail or fashionable framework names.
  • Use seven layers: goal, context, evidence, boundaries, process, output, and evaluation.
  • Test prompt versions on the same representative cases and keep changes that improve measurable results.
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03

21 min read

Context Engineering for AI Agents: Architecture, Memory, Retrieval and 10 Templates

Learn how to engineer context for reliable AI agents using source selection, retrieval, memory, compression, tool state, permissions, evaluation, and copy-ready templates.

  • Context engineering controls what an AI system knows at each step; prompt engineering controls how the current task is expressed.
  • Reliable context is selected, permission-aware, source-labelled, compressed, refreshed, and validated instead of being copied into one enormous prompt.
  • The ten templates turn context design into repeatable work for research, support, projects, agents, retrieval, memory, and handoffs.
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04

15 min read

AI prompt injection: how it works and how to reduce risk in real applications

Learn the difference between direct and indirect AI prompt injection, why prompt wording alone is not a security boundary, and how to build layered defenses.

  • Prompt injection happens when untrusted content changes an AI system’s behavior beyond the user’s intended task.
  • A stronger system separates instructions from data, limits permissions, validates actions and outputs, and requires approval for consequential operations.
  • Security testing must cover direct input, retrieved documents, webpages, email, tool results, memory, and every path that can place text in model context.
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05

16 min read

How to build a reusable prompt system and a professional team prompt library

Turn recurring AI tasks into tested, versioned prompt systems with owners, inputs, examples, evaluation cases, and team governance.

  • A reusable prompt is a task contract with variables, evidence rules, output requirements, and acceptance criteria.
  • A team library needs ownership, evaluation cases, version history, review status, and retirement rules.
  • Start with a small set of recurring, valuable tasks and expand only when usage and quality justify it.
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06

17 min read

Best AI Prompting Frameworks: RISEN, CO-STAR, CARE and RTF Compared

Compare RISEN, CO-STAR, CARE and RTF with practical examples, a selection guide, and the controls needed for reliable professional AI work.

  • RTF is the fastest framework for simple tasks; CARE adds a desired result and an example; CO-STAR is strongest for audience-sensitive communication; RISEN fits bounded, multi-step professional work.
  • Framework names are memory aids, not quality guarantees. Source rules, tool permissions, uncertainty handling, and verification still need to be added when the task carries risk.
  • Choose the smallest framework that removes meaningful ambiguity, then test it on representative inputs instead of judging it from one successful response.
Read the field guide