Prompt Engineering Best Practices: Tips, Tricks, and Tools

When ChatGPT launched in late 2022, it sparked an immediate rush to figure out how to get the most out of these powerful new language models. The practice was quickly dubbed ”prompt engineering,” and with it came breathless predictions that “prompt engineer” would become the hottest job title of the decade. LinkedIn filled up with self-proclaimed experts, and rumors of six-figure salaries circulated widely. Fast forward to now, and that initial hype cycle has cooled considerably.

But the underlying skill—crafting inputs that produce genuinely useful outputs—has only grown more important as AI tools have embedded themselves into real workflows. Whether you’re generating a quarterly analysis with Claude Sonnet 4.5, producing product photos with Google’s Nano Banana Pro, or debugging a complex function with OpenAI’s Codex CLI, the difference between a vague instruction and a well-crafted prompt may mean the difference between getting generic outputs and exactly what you need. Read on for prompt engineering best practices—from using few-shot examples to guide output format to breaking complex tasks into smaller steps—that will help you get useful results on the first try instead of the fifth.

What is prompt engineering?

Prompt engineering is the process of carefully designing the inputs you give to large language models to get desired outputs. It includes word choice, how you order instructions, what context you provide, and whether you include examples. At a more advanced level, it means knowing when to use techniques like few-shot prompting—providing input-output pairs so the model learns your desired format—or chain-of-thought prompting, where you ask the model to reason step-by-step before delivering a final answer. And despite the name, prompt engineering isn’t just for engineers: it’s for anyone using AI assistant tools like Claude, Gemini, or ChatGPT who wants better results, whether you’re writing code or drafting social posts.

Why is prompt engineering important?

As models like Llama 4, Grok 3, and DeepSeek-R1 grow more capable, the quality of your input increasingly determines the quality of your output. In other words, how you phrase a request shapes what you get back. A poorly structured prompt may require multiple back-and-forth exchanges to clarify intent, whereas a thoughtfully engineered prompt yields usable results on the first (or second) attempt.

• It reduces hallucinations and improves factual accuracy: Explicitly giving the model permission to say “I don’t know” when it lacks sufficient information makes it less likely to hallucinate and fabricate answers. Providing relevant context and source material gives the model a factual foundation to work from, rather than relying solely on training data.

• It unlocks better performance for code generation: When using tools like OpenAI’s Codex CLI or asking Claude to debug a script, prompts that include leading words like “import” or “SELECT” nudge the model toward the correct output pattern. Specifying the programming language, expected inputs and outputs, and edge cases to handle produces code that’s closer to production-ready.

• It makes complex reasoning more transparent and reliable: Chain-of-thought prompting asks the model to reason step-by-step before delivering a final answer, which is especially valuable for multi-step math problems or analytical tasks. This technique surfaces the model’s logic so you spot errors mid-process rather than discovering them in the final output.

• It gives you control over output format and structure: Few-shot prompting with input-output examples teaches the model the exact style, tone, or schema you’re looking for, whether that’s a JSON object, a bulleted summary, or a formal report.

• It improves results for image generation and multimodal tasks: When prompting image models like DALL-E 3 or Midjourney, specificity around lighting, composition, and style dramatically affects output quality. The same principle applies to vision-language models: asking Claude to analyze a chart step-by-step in structured tags yields more accurate data extraction than a generic “describe this image” request.

Types of prompting engineering techniques

Different tasks call for different approaches. A simple question might need nothing more than a clear instruction, while a multi-step analysis might require examples, structured reasoning, or breaking the work into stages. The techniques below range from basic to advanced, and they can (and should) be combined depending on what you’re trying to accomplish.

Zero-shot prompting

Zero-shot prompting is the most straightforward approach: you give the model a direct instruction without any examples. This works well when the task is clear, and the expected output format is obvious, like asking “What’s the capital of Japan?” or “Summarize this paragraph in two sentences.” The model relies entirely on its training to interpret what you want. Zero-shot is fast and requires minimal setup, but it falls short when you need a specific structure, tone, or style that isn’t implicit in the instruction itself.

Few-shot prompting

Few-shot prompting provides one or more input-output examples before your actual request, helping the model learn the pattern you’re looking for. For instance, if you want summaries in a specific format, you might show two examples of articles paired with your preferred summary style, then ask the model to summarize a third article the same way. This technique is especially useful when the desired output involves subtle formatting, tone, or conventions that are hard to describe in words alone, or when your data involves multiple different categorizations or classifications that benefit from clarification. Start with one example (one-shot), then add more if the output still isn’t matching your expectations.

Chain-of-thought (CoT) prompting

Chain-of-thought prompting asks the model to show its reasoning before delivering a final answer. The simplest version is adding “Think step-by-step” to your prompt, which encourages the model to work through the problem rather than jumping straight to a conclusion. For example, instead of asking “What’s 15% of 340?” you might prompt: “Calculate 15% of 340. Show your work step-by-step, then give the final answer.”

This technique shines when it comes to math problems, logic puzzles, and analytical tasks where intermediate steps matter. It also makes errors easier to spot—if the model’s reasoning goes wrong at step two, you get to catch it before it compounds into a bad final answer. Some models, like Claude’s extended thinking mode, automate this process, but manual CoT remains valuable when you need transparent, reviewable logic.

Tree-of-thought (ToT) prompting

Tree-of-thought prompting extends chain-of-thought by exploring multiple reasoning paths simultaneously rather than following a single chain. Instead of committing to one line of reasoning, the model generates several possible approaches, evaluates each one, and backtracks if a path isn’t working—similar to how humans solve complex problems through trial and error.

This technique is especially useful for tasks that require strategic planning, like game-playing or multi-step decisions, where an early wrong choice may derail the entire solution. ToT is more resource-intensive than simpler methods and is typically overkill for straightforward tasks, but it outperforms other techniques on problems that benefit from deliberate exploration and self-evaluation. A study conducted by researchers at Princeton and Google DeepMind found that tree-of-thought prompting achieved a 74% success rate on the Game of 24 benchmark—a math puzzle using four numbers to reach 24. This result was much higher than chain-of-thought (49%) and standard input-output prompting (33%)—but at a higher cost per case ($0.74 vs. $0.47 and $0.13).

Role or persona prompting

Role prompting assigns the model a specific identity, expertise, or perspective to shape the scope of how it responds. A prompt like “You are an experienced immigration attorney” will produce a different answer than “You are a travel blogger” when asked about visa requirements—the attorney focuses on legal accuracy, while the blogger might emphasize practical tips. This technique is useful when you need domain-specific vocabulary, a particular communication style, or responses calibrated to a certain audience.

That said, modern models are sophisticated enough that heavy-handed role assignments (“You are a world-renowned expert who never makes mistakes”) may actually backfire by limiting helpfulness. A lighter touch often works better: instead of assigning a role, try being explicit about the perspective you want, like “Analyze this investment portfolio with a focus on risk tolerance and long-term growth.”

Prompt chaining

Prompt chaining breaks a complex task into smaller sequential steps, where the output of one prompt becomes the input for the next. Rather than asking a model to “research this topic, outline an article, write the draft, and edit it for tone” in a single prompt, you’d handle each stage separately: first generate research notes, then feed those into an outline prompt, then expand the outline into a draft, and finally run an editing pass. This approach trades speed for accuracy—each individual prompt is easier for the model to execute well, and you get to review or adjust the output at each stage before moving on.

Prompt chaining is particularly effective for workflows that involve analysis followed by synthesis, like summarizing a document, grading the summary for completeness, then improving it based on that feedback. It’s typically implemented programmatically via API or AI workflow tools, but you can also do it manually in a chat interface by copying outputs between prompts. The manual approach gives you human-in-the-loop control over complex workflows, letting you apply your own judgment and make adjustments between steps.

Meta-prompting

Meta-prompting uses an LLM to generate, refine, or improve prompts rather than writing them yourself from scratch. Instead of crafting every detail manually, you provide a high-level description of what you want and let the model propose an improved version—or you use a more capable model to optimize prompts for a less capable one.

This technique is useful when you need reusable prompt templates, want to systematically improve prompts across a category of tasks, or are building applications where prompt quality directly impacts results at scale. Tools like ChatPRD are built around this idea—they take your high-level product concepts and turn them into structured prompts that AI can use to generate apps or features. Meta-prompting lets you work at the level of what you want rather than getting bogged down in the exact phrasing needed to make AI understand it.

Reflection prompting

Reflection prompting asks the model to critique, evaluate, or refine its own output before delivering a final answer. After generating an initial response, you prompt the model to review its work—checking for errors, inconsistencies, or areas for improvement—and then produce a revised version based on that self-assessment.

Use this approach for tasks where accuracy matters and a second pass catches mistakes that slipped through the first attempt. For example, after generating code, you might prompt: “Review the code above for bugs or edge cases, then provide a corrected version.” Reflection adds latency since it requires additional processing, but it often improves output quality on complex reasoning, writing, and problem-solving tasks.

10 prompt engineering best practices

Whether you’re generating code, drafting marketing copy, analyzing data, or building automated workflows, the following practices apply across use cases and prompting techniques. Think of them as universal habits that make any prompt more effective—regardless of the specific model you’re using or what you’re trying to accomplish.

1. Be as specific as possible

Specificity is key to obtaining the most accurate and relevant information from an AI when writing prompts. A specific prompt minimizes ambiguity, allowing the AI to understand the request’s context and nuance, preventing it from providing overly broad or unrelated responses. To achieve this, include as many relevant details as possible without overloading the AI with superfluous information so context stays focused. This balance ensures that the AI has just enough guidance to produce the specific outcome you’re aiming for.

When creating the best prompts for an AI, ask for the following specifics:

• Detailed context: Provide the AI with enough background information to understand the scenario you’re inquiring about. This includes the subject matter, scope, and any relevant constraints.

• Desired format: Clearly specify the format in which you want the information to be presented, whether it’s a list, a detailed report, bullet points, or a summary. Mention any structural preferences, such as headings, subheadings, or paragraph limits.

• Output length: Detail how long you want the AI’s response, whether that’s “3 paragraphs” or “250 words.”

• Level of detail: Indicate the level of detail required for the response, from high-level overviews to in-depth analysis, to ensure the model’s output matches your informational needs. For example, “Give me a five-sentence summary of this report” will produce a very different output than “Walk me through each section of this report and explain the key findings in detail.”

• Tone and style: A prompt asking for “a professional summary for stakeholders” will produce something very different from “a casual explainer for a blog audience.” If you have a specific lens in mind—formal, conversational, persuasive, technical—say so upfront rather than editing after the fact.

• Examples and comparisons: Asking the model to include analogies or concrete examples often makes the output more useful and easier to apply. A prompt like “Explain how neural networks learn and give me a real-world comparison” produces a more memorable response than one that stays purely abstract—like “Explain how neural networks learn,” which might give you a technically accurate but harder-to-grasp explanation

2. Supply the AI with examples

Incorporating examples into your prompts steers the AI’s responses in the desired direction. By providing examples as you write prompts, you set a precedent for the type of information or response you expect. This practice is particularly useful for complex tasks where the desired output might be ambiguous or for creative tasks with more than one relevant answer.

When you supply the AI with examples, ensure they represent the quality and style of your desired result. This strategy clarifies your expectations and helps the AI model its responses after the examples provided, leading to more accurate and tailored outputs.

Here are some example types you could provide to an AI to help guide it toward generating the best response possible:

• Sample texts: Share excerpts reflecting the style, tone, and content you want the AI to replicate. For instance, if you’re writing a company newsletter, paste a previous edition and prompt: “Write next month’s newsletter update in this same voice and format.”

• Data formats: To guide the AI’s output, provide specific data structures, such as table layouts or spreadsheet formats. You might include a sample row like “| Product Name | SKU | Price | Stock |” and ask the model to populate the rest based on a product list you provide.

• Document templates: Offer templates to ensure the AI’s response follows a desired structure and format. For a weekly status report, you could share your existing template with headers like “Accomplishments,” “Blockers,” and “Next Steps,” then ask the AI to fill it in based on your notes.

• Code snippets: Provide code examples if you need help with programming tasks to ensure correct syntax and logic. If you want a Python function that follows your team’s conventions, paste an existing function and prompt: “Write a new function that validates email addresses using the same structure and commenting style.”

• Graphs and charts examples: If you’re asking the AI to create similar graphics, share samples of visual data representation. You might include a screenshot of a bar chart from a previous quarterly report with the prompt: “Generate the data and labels for a chart like this showing Q3 performance.”

• Marketing copy: If you’re crafting marketing content, share ad copy that aligns with your brand’s voice for the AI to mimic. For example, paste a landing page headline and subhead from a previous product launch and prompt: “Write three variations of launch copy for our new API feature using this same tone and structure.”

3. Get better answers by providing data

Most LLMs now allow you to attach documents, images, spreadsheets, and other files directly to your prompts—and many also connect to tools like Google Drive, Notion, or GitHub to pull in context automatically. This makes it easy to incorporate specific data into your prompts… However, just because you can connect these tools doesn’t mean you should give unfettered access to all your business and personal files. Be selective: only share what’s necessary for the task at hand, and check whether your data might be used for model training—most platforms let you opt out of this in settings.

With AI privacy in mind, incorporating data into prompts improves the quality of AI-generated responses, providing a solid foundation for the AI to understand the context and craft precise answers. Provide data that includes numerical values, dates, or categories, organized in a clear and structured way. Also, remember to give context to the data and, when possible, to cite its source. When you give the AI concrete data with clear context, it does more than summarize—it analyzes trends, spots patterns, and surfaces insights you might have missed. This is especially valuable for tasks like financial analysis, market research, or performance reporting, where the output needs to be grounded in real numbers rather than general observations.

4. Specify your desired output

Whether you’re chatting with Gemini or ChatGPT, articulate the precise format and structure you expect in the response. Specify whether you require a detailed report, a summary, bullet points, or a narrative form to ensure the AI tailors its output to your needs. For instance, a prompt like “Give me a bulleted list of key takeaways” will produce something very different from “Write this up as a two-paragraph executive summary.”

Indicate any preferences such as tone, style, and the inclusion of certain elements like headings or subheadings. If you need a specific structure—like a blog post with an H1, three H2 sections, and a conclusion—say so explicitly rather than hoping the model infers it. By clearly defining your desired output, you guide the AI to deliver information that aligns with your intended use.

5. Provide instructions on what to do instead of what not to do

While LLMs continue to evolve and improve, they still stumble on negated prompts—instructions telling them what not to do, like “avoid bullet points.” A study conducted by researchers at KAIST found that larger models actually perform worse on these types of instructions, struggling more than smaller models to process negative framing. When constructing prompts for AI, it’s more effective to direct the system toward the desired action rather than detailing what it should avoid. This positive instruction approach reduces ambiguity and focuses the AI’s processing power on generating constructive outcomes.

Negative instructions often require the AI to interpret and invert them, increasing the cognitive load and potential for misunderstanding. By clearly stating the intended actions, you enable the AI to apply its capabilities directly to fulfilling the task at hand, improving the efficiency and accuracy of the response.

6. Give the model a persona or frame of reference

Persona prompting is a technique in its own right, but it also complements other strategies, such as few-shot prompting and chain-of-thought reasoning. By assigning the model a specific identity or expertise, you shape the tone, vocabulary, and depth of the response to match your needs.

This works across use cases:

• For generating images: “You are a fine art photographer with a focus on dramatic lighting.”

• For coding: “You are a senior backend engineer reviewing this Python script for security vulnerabilities.”

• For writing: “You are a tech journalist explaining this concept to a non-technical audience.”

The persona sets expectations and helps the model calibrate its output to the task at hand.

7. Try a prompt optimizer tool

If you’re running the same prompt repeatedly—for data extraction, content generation, or any structured task—consider using a prompt optimizer to automatically improve it. These tools test variations of your prompt, evaluate the results against your criteria, and iteratively refine the instructions until performance improves. The process is similar to what you’d do manually, but faster and more systematic: the optimizer proposes changes, measures their effectiveness, and retains the best-performing versions.

Here are some prompt optimization tools to explore:

• DSPy (with GEPA optimizer): Open-source framework that treats prompts as learnable parameters and uses reflection-based optimization.

• OpenAI Prompt Optimizer: Built-in tool for refining prompts using your own datasets.

• Opik Agent Optimizer: Supports multiple optimization algorithms, including evolutionary and Bayesian approaches.

• LangWatch: LLM ops platform with prompt optimization studio.

• PromptPerfect: Automatic optimization for text and image model prompts.

Even if you’re not ready to run an optimizer yourself, the takeaway still applies: treat prompt engineering as an iterative process, and don’t be afraid to test multiple variations to find what works best.

8. Split complex tasks into simpler ones

When dealing with complex tasks, breaking them into simpler, more manageable components (or prompt chaining) makes them more approachable for an LLM. This applies to everything from writing a multi-section report to building a financial model, debugging a codebase, developing a content calendar, or drafting a legal contract. Using step-by-step instructions helps prevent the AI from becoming overwhelmed and ensures that each part of the task is handled with attention to detail.

Additionally, this approach allows for easier monitoring and adjustment of each step, facilitating better quality control throughout the process. By compartmentalizing tasks, the AI also uses its resources more efficiently, allocating the necessary attention where it’s most needed.

9. Understand the model’s shortcomings

In crafting prompts for an AI, recognize the model’s limitations to set realistic expectations. Prompting AI to perform tasks it’s not designed for—or expecting certainty where it only offers probabilities—will lead to frustration and potentially misleading outputs.

Here are some known shortcomings of AI models:

• Hallucinations are inherent, not a bug: LLMs predict the next most likely word based on patterns—they don’t verify truth. When uncertain, they often generate plausible-sounding but fabricated information.

• Context window limits affect long inputs: Models only process a fixed number of tokens at once. In long documents or conversations, earlier context may get lost, leading to contradictions or forgotten details—a phenomenon sometimes called the “lost in the middle” effect.

• Training data has a cutoff: Models don’t have access to events or information after their training cutoff date unless they search the web or access external tools.

• Confidence doesn’t equal accuracy: A model often sounds equally confident about correct facts and complete fabrications. High confidence in a response is not a reliable indicator of correctness, so it remains important to validate facts yourself.

• Complex reasoning compounds errors: In multi-step tasks, small errors early on may snowball into larger inaccuracies by the end—especially in longer outputs or chain-of-thought reasoning.

• Nuance and ambiguity are challenging: Sarcasm, cultural references, and domain-specific jargon often trip up even advanced models, leading to misinterpretations or overly literal responses.

• Training data reflects societal biases: Models trained on internet text absorb biases around race, gender, political ideology, geographic regions, and economic status. The result can be outputs that favor certain perspectives or make unwarranted assumptions about demographics and context.

10. Take an experimental approach to prompting

Prompt engineering has evolved from a niche skill to a more established discipline, characterized by dedicated research, published best practices, and a growing library of techniques. But it’s still a field where experimentation pays off. The best practices in this guide—and others online—are a starting point, not a ceiling. These tools have only been commercially available for a couple of years, which means there are no established experts—just early practitioners learning alongside everyone else. You’ll often discover techniques specific to your use case, your model, or your workflow that aren’t documented anywhere.

Test your prompts iteratively. Small changes in wording, structure, or instruction order alter the output, and you’ll only learn how a model responds by trying things out. What works for code generation in Claude might not work the same way in GPT-5 and a prompt that produces great results for image generation in Midjourney might need a completely different approach in DALL-E. Keep notes on what works and what doesn’t across different models and tasks—whether you’re writing marketing copy, debugging Python scripts, or generating product visuals—and don’t be afraid to deviate from established patterns if you find something that performs better.

Defensive prompt engineering

If you’re building AI products that accept user input and pass it to an LLM, you need to think about prompt injection—a type of attack where malicious inputs manipulate the model into ignoring its original instructions or revealing sensitive information. This isn’t just a concern for developers building AI chatbots; it applies to any workflow where untrusted text gets processed by a model, from document summarizers to AI-powered search tools.

The following tactics help reduce your exposure, though no single defense is foolproof—layering multiple approaches provides the strongest protection.

• Use delimiters to separate instructions from user input: Wrapping user-provided content in clear markers like ###, “”", or XML tags helps the model distinguish between your system instructions and the data it should process. Pair this with instructions telling the model to treat the delimited content as inert data, not commands to follow.

• Sanitize and validate inputs before they reach the model: Scan incoming text for suspicious patterns like “ignore previous instructions,” “forget your rules,” or attempts to inject your own delimiter characters. Filtering known attack phrases and rejecting abnormally long or strangely formatted inputs adds friction for anyone trying to manipulate the system.

• Apply the principle of least privilege: Restrict your LLM application’s access to only the data sources and actions it genuinely needs, and use the lowest permissions possible. This doesn’t prevent prompt injection, but it limits the damage if an attack succeeds.

• Audit outputs before acting on them: If your LLM generates commands, code, or API calls, validate those outputs through rule-based checks or a secondary classifier before executing them. Treating the model’s output as untrusted—just like user input—prevents a successful injection from cascading into your systems.

• Test your prompts adversarially: Run red-team exercises where you actively try to break your own system using known injection techniques, jailbreak patterns, and edge cases. Regularly testing with inputs like “Ignore all above instructions and…” helps you identify vulnerabilities before attackers do.

Prompt engineering best practices FAQs

What is the difference between few-shot and zero-shot prompting?

Zero-shot prompting is when you give the AI a direct instruction without any examples, relying on the model to understand and complete the task from the instruction alone. Few-shot prompting involves providing one or more input-output examples before your actual request, which helps the model learn the format, tone, or style you’re looking for. Few-shot prompting is particularly useful when you need consistent formatting or when the task involves subtle patterns that are easier to show than describe.

Do I need to know how to code to use prompt engineering?

No—the core techniques are about communicating clearly with the model, not technical expertise. In fact, creatives often excel at prompt engineering because they’re practiced at describing concepts, setting tone, and iterating on feedback. That said, coding knowledge helps when you’re working with structured outputs like JSON, using the API directly, or building prompts into automated workflows.

Can prompt engineering help reduce AI hallucinations?

Yes, several techniques help. Providing relevant data or context gives the model a factual foundation to work from, and explicitly giving the model permission to say “I don’t know” reduces the likelihood of fabricated answers. Chain-of-thought prompting also helps because it forces the model to show its work, making errors easier to spot.

What are common mistakes to avoid in prompt engineering?

Being too vague is one of the most common issues—asking for “a summary” without specifying length, format, or focus leaves too much room for interpretation. Other frequent mistakes include overloading a single prompt with multiple unrelated tasks, using negative instructions instead of positive framing, and skipping iteration rather than testing and refining based on results.

What are the risks of prompt injection, and how do I prevent them?

Prompt injection occurs when malicious or accidental input manipulates an LLM into ignoring its original instructions or revealing sensitive information. To reduce risk, use delimiters to separate system instructions from user input, sanitize inputs for suspicious patterns, and validate the model’s outputs before acting on them.

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