Prompt Engineering
System prompts, few-shot patterns, chain-of-thought, structured output, red-teaming and meta-prompting — the craft of communicating with LLMs.
8 articlesin Prompt Engineering
Chain-of-Thought Prompting: Making LLMs Show Their Work
Zero-shot CoT, few-shot CoT, self-consistency, and when reasoning traces hurt as much as they help — the technique that unlocked multi-step reasoning in LLMs.
ReAct Prompting: Reasoning and Acting in LLM Agents
The Thought → Action → Observation loop that lets LLMs use tools, verify intermediate steps, and self-correct — the pattern behind most modern AI agents.
Prompt Injection: Attack Vectors and Defence in Production
Direct injection, indirect injection via retrieved content, jailbreaks, and the defence-in-depth architecture that keeps LLM applications secure.
Function Calling and Tool Use: Structured Outputs from LLMs
JSON mode, tool schemas, parallel tool calls, and the architecture patterns that let LLMs interact reliably with external APIs and databases.
Zero-Shot Prompting: What LLMs Know Without Examples
Clear task framing, persona, output format, and constraints — how to get accurate results from a single well-crafted prompt with no examples.
Few-Shot Prompting: Teaching LLMs by Example
Selecting, ordering, and structuring input-output examples to reliably steer model behaviour — the most effective prompting technique for consistent formatted output.
Structured Outputs from LLMs: JSON Mode, Function Calling, and Schema Enforcement
Practical patterns for getting reliable structured data from LLMs — JSON mode, function calling, schema validation, and fallback strategies.
Prompt Engineering as Software Engineering
Version control, testing, parameterization, and CI pipelines for prompts — treating prompt engineering with the same rigor as application code.