1. Define Clear Intent Start every prompt by explicitly defining what you want. Vague prompts like “Explain APIs” might lead to a broad answer, while a clear one like “Explain REST APIs with real-world Java examples” produces more focused results.

Tip: Begin prompts with verbs such as “Write,” “Summarize,” “Compare,” or “Generate.”

1. Provide Context AI performs best when given enough background information by including the purpose, audience, and format of your request.

Example:

“Write a report.”

“Write a 200-word executive summary about the benefits of using Docker for microservices.”

1. Role-Based Prompting Assigning a role helps AI adopt a specific tone and perspective. This guides the model to respond as an expert in that domain.

Example:

“Act as a senior Java developer and explain how to optimize Spring Boot applications for performance.”

1. Chain-of-Thought Prompting Encourage the model to “think step by step” to improve logical accuracy. This technique is especially effective for solving coding problems or debugging.

Example: “Explain your reasoning step by step before giving the final answer.”

1. Use Structured Prompts Organize your input using bullet points or numbered lists to clarify expectations.

Example: “Write a function in Python that does the following:

Reads a CSV file Filters rows by a column value Exports the result to a new file

1. Iterative Prompting Ask follow-up questions or refine prompts based on earlier responses.

Example: “Rewrite the previous code with better error handling and add comments for clarity.”

1. Specify Output Format Tell the model exactly how to structure the response — text, JSON, code, table, or markdown.

Example: “Provide the output as a JSON object containing keys ‘error’, ‘description’, and ‘solution’.”

1. Use Few-Shot Prompting Provide one or more examples to help the model learn your desired output style.

Example: “Example Input: [text] Example Output: [formatted summary]. Now apply the same format to this text: [new text].”

1. Zero-Shot Prompting Just describe the task clearly and directly which is a useful approach for general use cases or quick results.

Example: “Summarize this blog post in one paragraph.”

Context Window Optimization Large prompts can exceed the model’s memory. Keep essential details upfront, summarize unnecessary data, and focus on key context to optimize performance. 1.

Negative Prompting Tell the model what not to include. This prevents irrelevant or outdated content.

Example: “Write a tutorial on React Hooks without mentioning class components.”

1. Self-Critique Prompting Ask the AI to review or improve its own response. This technique enhances quality and accuracy.

Example: “Now analyze your previous answer and suggest how it could be more efficient.”

1. Multimodal Prompting If your model supports images or audio, combine text with visuals for richer context.

Example: “Based on this image of a dashboard, generate an SQL query that could produce the shown results.”

1. Prompt Chaining Break a complex task into multiple, smaller prompts, feeding each result into the next. This ensures consistency and better control over outcomes.

Example:

Step 1: “Generate a product description.” Step 2: “Summarize that description into an ad headline.”

1. Persona and Tone Control Define the writing tone or style. This helps match brand voice or target audience expectations.

Example: “Explain machine learning to beginners in a friendly and conversational tone.”

Conclusion Finally, Prompt engineering is about communicating effectively with the model. By applying these 15 techniques, developers can turn generative AI into a powerful coding assistant, documentation tool, and creative partner. To master the skills of prompt engineering, join Credo Systemz Prompt engineering course in Chennai. The key is to experiment, refine, and iterate because better prompts lead to smarter AI outcomes.