---
title: Prompting | Help | LLM Foundry
---
# Prompting
Think of LLMs as a smart, new employee (with amnesia) who needs explicit instructions. Here are some best practices:
## Use prompt optimizers
They rewrite your prompt to improve it. The [Playground](../playground) has one. Select the **Optimize** button.
## Be clear, direct, and detailed
Be explicit and thorough. Include all necessary context, goals, and details so the model understands the full picture.
- **BAD**: _Explain gravitation lensing._ (Reason: Vague and lacks context or detail.)
- **GOOD**: _Explain the concept of gravitational lensing to a high school student who understands basic physics, including how it’s observed and its significance in astronomy._ (Reason: Specifies the audience, scope, and focus.)
> When you ask a question, don’t just mumble. Spell it out. Give every detail the listener needs.
> The clearer you are, the better the answer you’ll get.
> For example, don't just say, Explain Gravitation Lensing.
> Say, Explain the concept of gravitational lensing to a high school student who understands basic physics, including how it’s observed and its significance in astronomy.
[](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/be-clear-and-direct)
[](https://platform.openai.com/docs/guides/prompt-engineering#tactic-include-details-in-your-query-to-get-more-relevant-answers)
[](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/clear-instructions)
## Give examples
Provide 2-3 relevant examples to guide the model on the style, structure, or format you expect. This helps the model produce outputs consistent with your desired style.
- **BAD**: _Explain how to tie a bow tie._ (Reason: No examples or reference points given.)
- **GOOD**:
_Explain how to tie a bow tie. For example:_
1. _To tie a shoelace, you cross the laces and pull them tight..._
2. _To tie a necktie, you place it around the collar and loop it through..._
_Now, apply a similar step-by-step style to describe how to tie a bow tie._ (Reason: Provides clear examples and a pattern to follow.)
> Give examples to the model. If you want someone to build a house, show them a sketch. Don’t just say ‘build something.’
> Giving examples is like showing a pattern. It makes everything easier to follow.
[](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/multishot-prompting)
[](https://platform.openai.com/docs/guides/prompt-engineering#tactic-provide-examples)
[](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/few-shot-examples)
## Think step by step
Instruct the model to reason through the problem step by step. This leads to more logical, well-structured answers.
- **BAD**: _Given this transcript, is the customer satisfied?_ (Reason: No prompt for structured reasoning.)
- **GOOD**: _Given this transcript, is the customer satisfied? Think step by step._ (Reason: Directly instructs the model to break down reasoning into steps.)
> Ask the model to think step by step. Don’t ask the model to just give the final answer right away.
> That's like asking someone to answer with the first thing that pops into their head.
> Instead, ask them to break down their thought process into simple moves — like showing each rung of a ladder as they climb.
> For example, when thinking step by step, the model could, A, list each customer question, B, find out if it was addressed, and C, decide that the agent answered only 2 out of the 3 questions.
[](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/chain-of-thought)
[](https://platform.openai.com/docs/guides/prompt-engineering#strategy-give-models-time-to-think)
[](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/break-down-prompts)
## Assign a role
Specify a role or persona for the model. This provides context and helps tailor the response style.
- **BAD**: _Explain how to fix a software bug._ (Reason: No role or perspective given.)
- **GOOD**: _You are a seasoned software engineer. Explain how to fix a software bug in legacy code, including the debugging and testing process._ (Reason: Assigns a clear, knowledgeable persona, guiding the style and depth.)
> Tell the model who they are. Maybe they’re a seasoned software engineer fixing a legacy bug, or an experienced copy editor revising a publication.
> By clearly telling the model who they are, you help them speak with just the right expertise and style.
> Suddenly, your explanation sounds like it’s coming from a true specialist, not a random voice.
[](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/system-prompts)
[](https://platform.openai.com/docs/guides/prompt-engineering#tactic-ask-the-model-to-adopt-a-persona)
[](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/assign-role)
## Use XML to structure your prompt
Use XML tags to separate different parts of the prompt clearly. This helps the model understand structure and requirements.
- **BAD**: _Here’s what I want: Provide a summary and then an example._ (Reason: Unstructured, no clear separation of tasks.)
- **GOOD**:
```xml
Provide a summary of the concept of machine learning.
Then provide a simple, concrete example of a machine learning application (e.g., an email spam filter).
```
(Reason: Uses XML tags to clearly distinguish instructions from examples.)
> Think of your request like a box. XML tags are neat little labels on that box.
> They help keep parts sorted, so nothing gets lost in the shuffle.
[](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/use-xml-tags)
[](https://platform.openai.com/docs/guides/prompt-engineering#tactic-use-delimiters-to-clearly-indicate-distinct-parts-of-the-input)
[](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/prompts/structure-prompts)
## Use Markdown to format your output
Encourage the model to use Markdown for headings, lists, code blocks, and other formatting features to produce structured, easily readable answers.
- **BAD**: _Give me the steps in plain text._ (Reason: No specific formatting instructions, less readable.)
- **GOOD**: _Provide the steps in a markdown-formatted list with ### headings for each section and numbered bullet points._ (Reason: Directly instructs to use Markdown formatting, making output more structured and clear.)
- **BAD**: _Correct the spelling and show the corrections._ (Reason: No specific formatting instructions)
- **GOOD**: _Correct the spelling, showing <ins>additions</ins> and <del>deletions</del>._ (Reason: Directly instructs to use HTML formatting, making output more structured and clear.)
> Markdown is a simple formatting language that all models understand.
> You can have them add neat headings, sections, bold highlights, and bullet points.
> These make complex documents more scannable and easy on the eyes.
## Use JSON for machine-readable output
When you need structured data, ask for a JSON-formatted response. This ensures the output is machine-readable and organized.
- **BAD**: _Just list the items._ (Reason: Unstructured plain text makes parsing harder.)
- **GOOD**: _Provide the list of items as a JSON array, with each item as an object containing `name` and `description` fields._ (Reason: Instructing JSON format ensures structured, machine-readable output.)
Note: Always use [JSON schema](playground#attachments) if possible.
> Imagine you’re organizing data for a big project. Plain text is like dumping everything into one messy pile — it’s hard to find what you need later.
> JSON, on the other hand, is like packing your data into neat, labeled boxes within boxes.
> Everything has its place: fields like ‘name,’ ‘description,’ and ‘value’ make the data easy to read, especially for machines.
> For example, instead of just listing items, you can structure them as a JSON array, with each item as an object.
> That way, when you hand it to a program, it’s all clear and ready to use.
## Prefer Yes/No answers
Convert rating or percentage questions into Yes/No queries. LLMs handle binary choices better than numeric scales.
- **BAD**: _On a scale of 1-10, how confident are you that this method works?_ (Reason: Asks for a numeric rating, which can be imprecise.)
- **GOOD**: _Is this method likely to work as intended? Please give a reasoning and then answer Yes or No._ (Reason: A binary question simplifies the response and clarifies what’s being asked.)
> Don’t ask ‘On a scale of one to five...’. Models are not good with numbers.
> They don't know the difference between a 2 versus a 3 score. ‘Yes or no?’ is simple. It’s clear. It’s quick.
> So, break your question into simple parts that they can answer with just a yes or a no.
## Ask for reason first, then the answer
Instruct the model to provide its reasoning steps _before_ stating the final answer. This makes it less likely to justify itself and more likely to think deeper, leading to more accurate results.
- **BAD**: _What is the best route to take?_ (Reason: Direct question without prompting reasoning steps first.)
- **GOOD**: _First, explain your reasoning step by step for how you determine the best route. Then, after you’ve reasoned it out, state your final recommendation for the best route._ (Reason: Forces the model to show its reasoning process before giving the final answer.)
> BEFORE making its final choice, have the model talk through their thinking. Reasoning first, answer second.
> That way, the model won't be tempted to justify an answer that they gave impulsively. It is also more likely to think deeper.
## Use proper spelling and grammar
A well-written, grammatically correct prompt clarifies expectations. Poorly structured prompts can confuse the model.
- **BAD**: _xplin wht the weirless netork do? make shur to giv me a anser??_ (Reason: Poor spelling and unclear instructions.)
- **GOOD**: _Explain what a wireless network does. Please provide a detailed, step-by-step explanation._ (Reason: Proper spelling and clarity lead to a more coherent response.)
> If your question sounds like gibberish, expect a messy answer. Speak cleanly.
> When you do, the response will be much clearer.
## Example prompts
- **Get Alt Text for an image**. Use an image model, and say `Describe the image for a blind person. Follow the WCAG guidelines for alt-text.`
- **Compare 2 images**. Upload 2 images and say, `Compare these 2 similar images. Describe both images in detail, including every word used. Then explain the difference between the two visuals (including every word) to a blind person in a structured way.`