Compare how prompts are structured for different large language models, and discover helpful pointers on how to get better outputs from each.
Introduction
Writing queries for a language model is a skill that can feel both creative and technical. The way you phrase your request (often called your “prompt”) influences the quality of the text you receive back. Because different large language models (LLMs) have different strengths, understanding these differences becomes an important part of getting the most value out of them.
In this guide, we’ll explore how prompts work for various LLMs, how to tailor your phrasing to each model, and a bunch of practical tips that’ll make your life easier. We’ll go through detailed sections on the basics, the differences among popular models, strategies for getting desired results, and common pitfalls to avoid. Whether you’re a newcomer or someone with a bit of experience, you’ll find plenty of insights here to up your game.
Getting Started with Prompt Engineering
What is Prompt Engineering?
Prompt engineering focuses on creating effective triggers for language models. The “prompt” is the text you give to the model, asking it to produce an answer. To draw a parallel: if you were having a conversation with a buddy and wanted them to give you a detailed response, you’d try to ask them a clear question or statement. That same logic applies to LLMs.
Why Does it Matter?
If you just type vague statements, you’ll usually get lackluster replies. But with a concise and clear request, you give the model a structure and context, making it simpler for it to stay on track and provide content that matches your needs. This doesn’t just save time—it ensures you get accurate, relevant material.
Key Considerations
- Clarity: The more precise your questions, the better the answers.
- Context: Share relevant details, like the role, scenario, or tone you’re aiming for.
- Style: Make your request direct. If you want your output formatted in bullet points, mention that.
- Limitations: Keep in mind that every model has constraints, so you might not get the perfect reply every time.
Popular Large Language Models and Their Differences
There’s a growing list of LLMs, but let’s zero in on a few that have gained attention. Knowing their individual quirks makes it easier to shape your prompts effectively.
GPT-Family Models
OpenAI’s GPT lineup has been known for its proficiency in generating coherent text across a range of topics. These models can handle tasks like answering questions, creating summaries, and more, often with a casual, human-like quality. They also tend to follow instructions closely when given the right structure.
Prompts for GPT Models
- Conversational Cues: Start your prompts as if you’re talking to someone knowledgeable.
- System Messages: Some versions let you provide a “system” message for setting the context. This is useful for specifying the role of the AI, like “You are a helpful tutor.”
- Formatting: If you need lists, bullet points, or certain headings, specify that clearly.
- Length Guidance: Ask for short or long replies if you have length constraints.
BERT-Based Models
BERT (Bidirectional Encoder Representations from Transformers) and its variants focus more on tasks like classification, question-answer retrieval, and content analysis rather than creative text generation. They excel at tasks that require deeper understanding of text, such as extracting keywords or analyzing sentiment.
Prompts for BERT Models
- Task-Oriented: BERT is built around tasks like fill-in-the-blank (masked language modeling) or next-sentence prediction, so you’ll need prompts that align with these tasks.
- Clear Targets: Be explicit about what you want BERT to do (e.g., “Identify the emotion in the following sentence...”)
T5 and Similar Models
T5 (Text-To-Text Transfer Transformer) is designed to transform text into new text forms. If you want to transform a summary into a longer explanation, T5 could be a solid choice. T5 takes any problem and treats it as a text-to-text endeavor, which simplifies your prompt engineering a bit—just remember to specify the type of text transformation clearly.
Prompts for T5
- Short but Specific: Give it direct instructions like, “Convert this sentence into passive voice: …” or “Summarize this article in two lines…”
- Task Prefix: T5 often benefits from a prefix that clarifies the job. For example, “summarize:” or “translate English to Spanish:”
Other Notable Models
Models like BLOOM, LLaMA, and others are also worth exploring. Each has its own strong points, but the general rule remains: be structured in what you ask for and include enough background details.
Foundations of Crafting a Good Prompt
1. Brevity and Clarity
Too many words can bury your main question. Keep it straight to the point. At the same time, avoid being so brief that you lose context. Find the sweet spot that provides enough direction.
2. Clear Instructions
You can ask for a specific format or tone, like “Write in a formal style” or “List the main points in bullet form.” Providing explicit instructions helps the model follow your desired structure.
3. Context and Constraints
Let’s say you need a summary of a five-paragraph text. You could instruct, “Summarize the main points of the following text in about three sentences.” By setting a constraint (three sentences) and specifying the scope (the five paragraphs), you reduce guesswork for the model.
4. Examples
If you can, show what a successful answer might look like. For instance, if you want to teach the model how to create recipes, provide a sample and specify the style: “Give me a recipe in a similar format.”
5. Role Play
Sometimes, telling the model to act in a certain role can yield more targeted responses. You could say: “You are an enthusiastic travel blogger. Describe the best spots in Tokyo for first-time visitors.” This role-based approach sets the stage, giving the model direction on tone and point of view.
Practical Strategies for Different LLMs
Let’s break down some approaches you can use, depending on which model you’re dealing with.
GPT-Family: Conversation-Driven Approach
- System and User Messages: In many GPT variants, you can set a system-level message to shape the entire discussion. Then, user-level prompts refine specific requests.
- Multi-Step: If you need step-by-step solutions, ask for a list, then follow up for more details.
- Tone Control: GPT can emulate different styles, from casual to more professional, by specifying that tone upfront.
BERT-Based: Task-Focused Approach
- Masked Language: For fill-in-the-blank style prompts, define the placeholders carefully.
- Classification and Extraction: Formulate questions like “Identify the key subjects in the following paragraph,” or “Is the tone of the following sentence positive, negative, or neutral?”
T5: Text-to-Text Approach
- Task Labels: Make it clear what type of transformation you need, such as “translate:” or “summarize:”.
- Data Format: If you have structured data you need turned into text (or vice versa), explain that format clearly so T5 knows how to handle it.
Multi-Model Strategy
If you happen to be using more than one LLM, you can play to each model’s strengths. For instance, use a BERT model for classification tasks (like sorting emails into categories), then pass the sorted content to GPT for more natural-sounding responses.
Tips and Tricks to Level Up Your Prompt Craft
1. Ask Questions or Give Commands?
Decide if it makes more sense to phrase your statement as a question or an instruction. For instance, if you need a straightforward answer, a direct question might be best (“Why is the sky blue?”). But if you want something like a blog outline, you might say, “Create an outline for a piece on the history of photography.”
2. Use Constraints and Boundaries
Nobody likes reading a wall of text when all they want is a bulleted list. Give your models guardrails such as format or length. For example:
- “Summarize this paragraph in three bullet points.”
- “Create a friendly greeting message under 20 words.”
3. Add Extra Context
If you’re referencing a particular brand, product, or situation, share those details. A typical request might be: “You’re writing for a brand that sells eco-friendly clothes. Draft a brief product description highlighting sustainability.”
4. Iterate and Refine
Don’t hesitate to adjust your prompt if your first attempt isn’t perfect. Refine your instructions, add or remove details, and see how it changes the outcome. Iteration is normal, and each pass will improve the clarity.
5. Beware of Tricky Topics
LLMs can sometimes struggle with sensitive material or may produce something inaccurate. If you’re dealing with complex or touchy topics, keep an eye on the output and provide additional guidance as needed.
Expanding Your Prompt Engineering Toolbox
So far, we’ve covered the fundamentals. Let’s take it a step further by exploring some advanced tactics and considerations you may find helpful.
Using Personas to Guide Style and Content
Giving your LLM a “persona” can be one of the easiest ways to nudge it toward a specific style. By stating, “You are a friendly tour guide,” or “You are an experienced historian,” you set a certain voice or perspective right from the start.
Best Practice
Try to mention your persona at the beginning of your prompt. Also, consider adding extra instructions if you want the content to be more detailed, comedic, or even authoritative.
Structured Inputs and Outputs
When you have a complex request, break it down into smaller structured pieces. For example, if you’re gathering data on a movie for a research assignment, you might say:
“For the following movie, provide:TitleMain actorsGenreA short summary (50 words max)”
This approach makes it simpler for the model to deliver organized results, which is especially useful when you want to feed the output into a database or further analysis tool.
Using Few-Shot and Zero-Shot Learning
- Zero-Shot: This is when you ask a model to do something it’s never explicitly been trained on. For instance, “List five synonyms for the word ‘beautiful’” without providing any examples.
- Few-Shot: In a few-shot scenario, you offer a small number of samples. For example, if you want the model to generate jokes, you might provide two short jokes as examples, then ask the model to create a similar one.
Chain-of-Thought or Step-by-Step Reasoning
You can often get better explanations or justifications if you ask the model to “think aloud” or “explain your reasoning.” For example:
“Solve the following math problem step by step, explaining your method as you go…”
Models like GPT can be guided to show how they reason about a topic, which can be handy for tasks like solving puzzles or demonstrating logic.
Handling Sensitive or Restricted Subjects
Sometimes, you’ll be dealing with content that has age, cultural, or other limitations. While each model has specific content restrictions, you can also incorporate disclaimers in your prompt, such as:
“The content below is meant for an adult audience. Only continue if you’re comfortable with more mature themes.”
By clarifying such details, you increase the likelihood of receiving an answer that’s both relevant and aligned with your audience’s requirements.
Common Challenges and How to Overcome Them
1. Rambling or Off-Topic Replies
Sometimes, language models can go off on a tangent. If that happens, try to refine your query. Specify the scope or desired angle right at the start, and if the model still deviates, break your request into smaller segments.
2. Repetitive Answers
If your text starts looping around the same phrases, it may be because your prompt is too vague. Include variety in your query, mention you’d like more synonyms, or focus on a different angle.
3. Handling Limitations
Different models have different token or length constraints. Be mindful of these when you’re making large requests. If you run into length issues, try summarizing or splitting your content into sections.
4. Mismatched Tone
If you’re aiming for something witty but get a flat response, emphasize the style again. Sometimes it helps to give an example sentence in the exact tone you’re looking for.
5. Incorrect Facts
LLMs might produce statements that sound convincing but are actually false or outdated. For factual content, use external verification. Incorporate hints in your prompt to encourage the model to focus on well-documented topics or to indicate any sources it should reference.
New Subtopics to Strengthen Your Prompt Approach
Experimenting with Different Output Formats
Prompts don’t have to generate only text paragraphs. You can ask for bullet points, JSON structures, or even mock outlines for slides. For instance, if you need a quick outline for a presentation, you can say:
“Prepare a structured outline in bullet form for a 10-minute talk on effective communication skills. Include an intro, three key points, and a conclusion.”
Contextual Layering
Consider adding multiple layers of context:
- Background: “You are an expert event planner…”
- Objective: “Plan a wedding with a small budget…”
- Constraints: “The wedding must be held outdoors and only last three hours.”
By layering context and objectives, you reduce confusion for the model, ensuring it targets the right details.
Testing with Various Sample Inputs
To confirm your prompt’s effectiveness, test it with multiple sample inputs. If your prompt is supposed to handle a range of topics, feed it at least a few different examples. See how the model responds and note any areas that need refining. This iterative testing helps you build a robust prompt that can handle various contexts without confusion.
Collaboration Between Models and Plugins
In some workflows, you might combine an LLM with plugins or other specialized tools. For example, you could use one tool to fetch real-time weather data, then feed that data to your LLM. This approach can significantly improve the richness and reliability of your final output. While not strictly a prompt engineering method, the way you integrate these tools or modules affects your prompt and your results.
Monitoring and Feedback Loops
If you’re using an LLM for a production setting or a large project, consider setting up a feedback loop. Track how the outputs are used, whether they meet your quality standards, and if adjustments are required. Over time, this feedback helps fine-tune not just the model (if that’s within your control) but also your prompt design.
Conclusion
Prompt engineering is an ongoing learning process. Even if you master one model, you’ll find that a new version or a different architecture may require a fresh approach. The key is to stay curious and experiment. By focusing on clarity, context, and iterative refinement, you’ll quickly discover that getting great responses from a wide range of LLMs isn’t just about clever hacks—it’s about consistent, thoughtful practices.
Whether you’re tapping into the conversational flair of GPT, the analytical accuracy of BERT, or the transformation powers of T5, a bit of planning and experimentation can bring you closer to the results you want. So go ahead: craft your prompts with a little creativity, watch how they evolve with each change, and remember that every prompt is a chance to learn something new.
