Introduction to prompt engineering
Prompt engineering is the practice of designing effective instructions that guide Large Language Models (LLMs) toward generating desired outputs. As LLMs become more integrated into workflows, mastering prompt engineering is crucial for harnessing their full potential. It involves a combination of instruction, context, and iterative refinement to achieve accuracy, relevance, and safety in AI-generated text. This guide explores the fundamental techniques and best practices that will enable you to communicate with LLMs more effectively.
A well-crafted prompt acts as a precise set of instructions for the model. The difference between a vague prompt and a specific, well-structured one can be the difference between a generic, unhelpful response and a detailed, insightful one. For developers, writers, and researchers, prompt engineering is not just about getting the right answer; it’s about controlling the output’s format, tone, and depth, making the LLM a reliable partner in complex tasks.
Key prompting techniques
Zero-shot prompting
Ask the model to perform a task without examples. Works best for straightforward questions or translations.
Translate the following English text to French: “Hello, how are you?”
Few-shot prompting
Provide several examples to teach the model the format you expect.
**English:** The cat is happy.
**Sentiment:** Positive
**English:** The weather is gloomy.
**Sentiment:** Negative
**English:** The new restaurant is okay, but the service was slow.
**Sentiment:**Chain-of-thought prompting
Encourage the model to reason through intermediate steps by instructing it to think step-by-step. Ideal for reasoning and planning tasks.
Persona or role prompting
Assign the model a persona (“You are a CTO…”) to control voice, expertise, and format.
Advanced prompting
Self-consistency
Run the same prompt multiple times with higher temperature, then vote on the most common answer to reduce reasoning errors.
Tree-of-thought (ToT)
Explore multiple reasoning branches before converging on the best solution—useful for planning or creative ideation.
Retrieval-augmented generation (RAG)
Pair prompts with retrieved documents to ground the model in proprietary or recent data.
Workflow visualised
graph TD
A[Define Goal] --> B{Is task simple?}
B -- Yes --> C[Use Zero-shot]
B -- No --> D{Need specific format?}
D -- Yes --> E[Use Few-shot]
D -- No --> F{Requires reasoning?}
F -- Yes --> G[Chain-of-Thought]
G --> H{Need higher accuracy?}
H -- Yes --> I[Self-Consistency]
H -- No --> J[Tree-of-Thought]
F -- No --> K{Need external knowledge?}
K -- Yes --> L[RAG]
K -- No --> M{Need specific tone?}
M -- Yes --> N[Persona Prompting]
M -- No --> O[Iterate and refine]
C --> O
E --> O
J --> O
L --> O
N --> O
O --> P[Desired Output]
style A fill:#7aa2f7,stroke:#bb9af7,stroke-width:3px
style P fill:#9ece6a,stroke:#e0af68,stroke-width:3px
style O fill:#2ac3de,stroke:#7aa2f7,stroke-width:3px
Best practices
- Be specific: Define scope, tone, length, and output format.
- Provide context: Supply relevant data or constraints.
- Structure clearly: Use numbered steps, tables, or JSON skeletons in the prompt.
- Iterate deliberately: Review outputs, adjust instructions, and log winning prompt formats in a shared library.
- Automate evaluation: Pair prompts with guardrail tests to check toxicity, correctness, and completeness.
Operationalising prompts
- Create a versioned prompt registry (Git, database, or Starlight content) with owners and success metrics.
- Attach prompts to automated evaluations (unit tests or eval harnesses) before shipping them into production flows.
- Review prompt performance in regular AI Ops syncs and retire underperforming variants.
Conclusion
Treat prompt engineering as an evolving product discipline. With structured experimentation, documentation, and testing, LLM-powered systems become predictable collaborators instead of unpredictable black boxes.