How Domain Experts Learn Prompt Engineering
Learn how domain experts enhance prompt engineering for large language models through collaboration, clear strategies, and continuous improvement.
Prompt engineering is the skill of crafting precise instructions to get accurate and meaningful outputs from large language models (LLMs). For domain experts - like financial analysts, doctors, or lawyers - this involves designing prompts that align with their industry’s specific needs and terminology. The process is hands-on, iterative, and benefits from collaboration between domain specialists and engineers.
Here’s a quick breakdown of how domain experts approach prompt engineering:
- Understand LLMs: Learn how LLMs process inputs, including token limits, context windows, and settings like temperature for predictable or creative outputs.
- Use Effective Strategies: Techniques like direct instructions, examples (few-shot prompting), step-by-step reasoning, role-based prompts, and setting clear constraints improve results.
- Write Clear Prompts: Provide context, use professional language, specify formatting, and refine prompts iteratively.
- Collaborate in Teams: Workshops and labs help domain experts and engineers co-design prompts, test them live, and refine based on feedback.
- Track and Improve: Use metrics like relevance, efficiency, and consistency to continuously refine prompts for better performance.
Tools like Latitude simplify this process by enabling teams to test, refine, and document prompts in a shared workspace. The key is consistent practice, collaboration, and tracking improvements to meet specific goals.
Basic Skills and Concepts for Prompt Engineering
Understanding Large Language Models (LLMs)
To craft better prompts, it’s essential to understand how Large Language Models (LLMs) function. Unlike humans, LLMs don’t truly "understand" language. Instead, they predict the next word (or token) based on patterns they’ve learned from massive datasets.
When you send a prompt to an LLM, it processes your input by breaking it into tokens and then predicts the most likely sequence of tokens to generate a response. The temperature setting plays a big role here. Lower settings (like 0.1–0.3) produce more predictable and focused outputs, which is great for tasks requiring precision or compliance. On the other hand, higher settings (0.7–1.0) encourage more creative and varied responses, making them ideal for brainstorming.
Another key factor is the model’s context window, which limits how much information it can process at once. These windows typically range from 4,000 to 128,000 tokens. LLMs give more weight to information at the beginning and end of a prompt, so placing critical instructions in these areas often leads to better outcomes. By understanding these mechanics, you can design prompts that align with the model’s strengths, improving the quality of responses.
Key Prompting Strategies
Effective prompting starts with clear strategies tailored to the task at hand. Here are some proven approaches:
- Direct instruction prompting: Be explicit about what you want. For instance, instead of asking, "What do you think about this contract?", a legal expert might say, "Review this contract for potential liability issues and highlight any clauses that could expose our company to financial risk."
- Few-shot prompting: Provide examples of the desired output. A marketing professional, for example, might share a couple of successful email subject lines and their open rates before asking the model to generate new ones.
- Chain of Thought (CoT) prompting: Encourage systematic reasoning by adding phrases like "Let’s think through this step by step." Financial analysts often use this method to break down complex investment evaluations.
- Role-based prompting: Ask the model to adopt a specific perspective. Starting with "As a senior software architect..." can guide the model to use relevant terminology and focus on domain-specific considerations.
- Constraint-based prompting: Set clear boundaries for the response. For example, "Provide exactly three recommendations, each in 50 words or less" ensures outputs meet specific requirements.
Once you’ve chosen the right strategy, structuring your prompt clearly is crucial to getting the desired results.
Writing Clear and Contextual Prompts
Clear, detailed prompts almost always outperform vague ones. For instance, rather than saying, "Help me with this report", you could specify, "Summarize the key findings from this quarterly sales data, focusing on regional performance differences and identifying the top three growth opportunities."
Here are some tips for writing better prompts:
- Context layering: Start by providing broader context, then narrow it down. For example, "Our company is launching a new SaaS product for small businesses" sets the stage, followed by specifics like, "We need to price it competitively", and finally, "Generate five pricing strategy options with pros and cons."
- Use professional terminology: Include relevant terms naturally but ensure there’s enough context. A healthcare professional might write, "Analyze this patient case study for potential drug interactions, considering both prescription medications and over-the-counter supplements that could affect treatment efficacy."
- Specify output formatting: If you need the response in a particular format, say so. For example, "Provide this analysis as bullet points, using medical terminology appropriate for physician communication."
- Iterative refinement: Don’t expect perfection on the first try. If the initial output isn’t quite right, refine your prompt. Add examples, clarify constraints, or adjust instructions based on what the model missed.
- Prevent common errors: Anticipate areas where the model might misinterpret your request. For instance, if you’re asking for financial projections, specify the time frame, currency, and whether you want conservative or optimistic estimates. Similarly, for legal tasks, clarify the jurisdiction and whether citations are required.
Working Together in Prompt Engineering
How Workshops Help Teams Work Together
Workshops provide a structured space where domain experts and engineers collaborate directly, breaking down the communication barriers that often slow AI projects. Unlike back-and-forth email chains or delayed feedback loops, workshops allow for real-time discussions and immediate adjustments to prompts. This live interaction ensures smoother progress and quicker solutions.
The most effective workshops take a hands-on approach, focusing on real-world use cases rather than abstract scenarios. Domain experts bring their knowledge of business needs, regulations, and user expectations, while engineers contribute their understanding of model capabilities, performance tuning, and technical limitations. Together, they can spot potential issues early and come up with practical solutions.
A key feature of these sessions is live prompt testing, which fosters alignment between teams. For example, when a compliance expert sees how their carefully crafted legal prompt performs in practice, they can make on-the-spot adjustments. Similarly, engineers can observe how industry-specific terms influence a model's response and suggest tweaks to improve results. This immediate feedback loop speeds up learning and refinement.
Workshops also help set realistic expectations about what AI can and can’t do. Domain experts might realize their assumptions about AI capabilities need adjustment, while engineers gain a deeper understanding of the intricate requirements specific to certain industries. This mutual understanding helps prevent costly misunderstandings later in the process.
Effective Co-Design and Prototyping Methods
After workshops, teams often move to structured prototyping to refine their prompt strategies. Co-design works best when it involves iterative cycles that balance creativity with careful evaluation. A common starting point is rapid prototyping, where domain experts and engineers collaborate to create multiple prompt variations for a single use case.
One effective method is paired programming, where a domain expert explains the business context while an engineer crafts the technical prompt in real time. This approach ensures that prompts reflect both the domain knowledge and the technical precision needed for real-world applications.
The prototyping process benefits greatly from systematic variation testing. Teams can experiment with different prompt formats - adjusting detail levels, rephrasing instructions, or modifying the context. By comparing these variations side by side, they can identify the most effective strategies for their specific needs and understand why certain approaches work better.
Scenario-based prototyping is another valuable tool, helping teams prepare for edge cases and unusual inputs. Domain experts contribute examples of challenging real-world scenarios, while engineers design prompts to handle these situations effectively. This collaboration ensures the system is more reliable, even under unexpected conditions.
Version control is essential throughout the co-design process. Teams need to track not only the prompts but also the rationale behind design decisions and performance data from testing. This documentation helps maintain continuity across multiple sessions and ensures new team members can quickly understand the evolution of the project.
Building a Shared Vocabulary
Clear communication is a cornerstone of successful prompt engineering. Establishing a shared vocabulary between domain experts and engineers eliminates confusion and streamlines collaboration. Without this common language, teams can waste valuable time clarifying basic concepts instead of focusing on refining prompts.
One major challenge is translating technical terms. Domain experts often use specialized jargon, while engineers require precise, actionable language. To bridge this gap, many teams create glossaries that define terms in ways both groups can understand and apply.
The process of building this shared language should be two-way. Engineers learn domain-specific terms and how they influence prompt design, while domain experts become familiar with AI concepts like temperature settings, token limits, and context windows. This mutual exchange leads to more productive discussions and better optimization strategies.
Using standardized prompt templates can also help maintain consistency while addressing specific domain needs. These templates provide placeholders for domain knowledge, standardize instruction formats, and outline approaches for handling edge cases. When everyone works from the same structure, it’s easier to share insights and scale solutions across projects.
Documentation plays a vital role in preserving this shared vocabulary. Successful teams maintain living documents that capture not only the current terminology but also how their understanding has evolved over time. These records help onboard new members and ensure valuable insights aren’t lost as projects progress.
Finally, regular vocabulary review sessions keep everyone aligned. These quick check-ins allow teams to identify and clarify unfamiliar terms, ensuring consistency across different projects and contexts. By maintaining a shared understanding, teams can work together more effectively as they refine and iterate on their prompt designs.
Hands-On Labs with Prompt Engineering Tools
Using Latitude for Prompt Engineering
Latitude provides an open-source platform designed to simplify prompt engineering for domain experts. This tool bridges the gap between deep domain knowledge and AI capabilities, offering a user-friendly environment for experimenting with prompts. Even without coding experience, experts can tweak prompts, test variations, and observe real-time results, making the process both accessible and efficient.
By enabling domain experts to directly interact with prompts, Latitude accelerates the learning curve. Experts can see how their industry insights translate into functional prompts, while engineers contribute technical context about model behavior. Together, they document their findings in a shared workspace, supported by community resources that address common challenges in prompt design.
This collaborative environment naturally transitions into structured labs, where prompt engineering techniques are refined further.
Structured Lab Exercises
Structured lab exercises build on collaborative workshops, focusing on systematic experimentation. These labs start with simple prompt adjustments and gradually move into more complex scenarios, encouraging rigorous testing and deeper insights.
One key method is comparative prompt testing, where domain experts create multiple versions of a prompt for the same task. By altering factors like instruction clarity, context, or output formatting, they can test these variations side by side. This approach helps identify which strategies work best for specific tasks and domains.
Another essential technique is batch testing, which evaluates prompt performance across a variety of inputs at once. Instead of testing one example at a time, this method highlights patterns in how prompts handle diverse scenarios. For instance, it can reveal how slight changes in phrasing impact consistency across different queries.
Labs also focus on output quality monitoring, teaching experts to spot when responses are technically correct but miss critical domain-specific nuances. Exercises emphasize iterative improvements, encouraging small, measurable adjustments rather than sweeping changes that make it harder to pinpoint what worked or didn’t.
Version Control and Scenario-Based Testing
To refine and scale prompt engineering strategies, version control and scenario-based testing are indispensable tools. Version control allows experts to track changes, document their reasoning, and analyze performance data. This process builds a knowledge base that explains why specific revisions succeed and helps maintain clarity in the development process.
Logs play a crucial role here. Domain experts record what was modified, why they made the changes, and their hypotheses about expected outcomes. Tracking results systematically helps build a library of effective approaches tailored to their unique needs.
Scenario-based testing introduces prompts to real-world challenges. Instead of relying on idealized inputs, experts create scenarios that reflect the messy, ambiguous, or incomplete information systems often encounter. These tests include edge cases and failure modes, leveraging field knowledge to anticipate and address potential issues.
To ensure progress doesn’t come at the cost of stability, regression testing verifies that new improvements don’t disrupt existing functionality. Test suites are maintained to confirm that prompts continue to handle previously solved problems while expanding their capabilities.
Testing and Improving Prompts Over Time
Why Continuous Improvement Matters
While structured labs and collaborative workshops provide a strong foundation, the real key to success lies in continuously refining prompts. Prompt engineering isn’t a one-and-done task. As new data, edge cases, and shifting requirements emerge, prompts need to evolve. What might work flawlessly in a controlled lab environment can stumble when faced with the unpredictability of real-world applications.
This is where systematic analysis becomes essential. Domain experts play a critical role in spotting issues that might fly under the radar of standard technical evaluations. For instance, a financial analyst might notice that a prompt can calculate loan payments accurately but fails to account for differences in regional interest rate regulations. These nuanced insights are invaluable and go beyond what generic testing frameworks can reveal.
The secret to effective refinement lies in small, incremental adjustments rather than complete overhauls. For example, if a healthcare-related prompt struggles with interpreting abbreviations, a simple tweak to add context can significantly improve its accuracy without disrupting its overall functionality.
Equally important is documenting changes during this iterative process. By recording not just what was modified but the reasoning behind each adjustment, domain experts create a valuable knowledge base. This documentation ensures that teams can understand the rationale behind changes, preserving insights for future use and helping maintain consistency over time.
Tracking Metrics for Success
Once changes are made, measuring their impact is critical. Generic accuracy scores are often too broad to capture the subtleties that matter most in specific domains. Instead, tailored metrics are needed to reflect real-world priorities.
Take relevance metrics, for example. A legal expert analyzing contract-related prompts might care less about processing speed and more about whether the system flags critical clauses, such as liability terms or unusual conditions that require human review. These metrics align directly with the priorities of the domain.
Efficiency measurements are another key focus. It’s not just about whether the prompt gets things right but also how much human intervention is needed. A marketing professional, for instance, might track how often AI-generated campaign copy requires heavy edits versus how often it’s ready to use with minimal tweaks.
Consistency tracking is equally important. Patterns of inconsistency can reveal underlying issues. For example, a customer service manager may notice that while complaint resolution prompts handle product-related issues well, they falter when addressing billing disputes. These insights guide targeted refinements for specific scenarios.
The best approach starts with establishing baseline metrics before implementing any changes. By comparing results over time, domain experts can transform subjective observations into clear evidence of improvement. This data-driven approach ensures that refinements are not only effective but also measurable.
Practical Use Cases for Prompt Engineering
The true value of prompt engineering shines in solving domain-specific challenges. When applied to real-world scenarios, refined prompts can address complex problems with precision.
Regulatory compliance is one such area where domain knowledge is indispensable. Insurance professionals, for example, design prompts to process claims while ensuring compliance with state-specific regulations. Their expertise allows them to craft prompts that flag potential compliance issues early, avoiding costly mistakes.
In quality assurance, domain experts can pinpoint subtle but critical discrepancies. Manufacturing specialists, for instance, use prompts to analyze supplier reports, leveraging their understanding of industry standards to detect issues that might otherwise go unnoticed but could signal serious quality concerns.
Customer communication is another area where refined prompts make a big difference. Healthcare administrators, for example, create prompts for patient interactions that balance medical accuracy with empathy. These prompts ensure that patients receive clear, accurate information delivered in a sensitive and understanding tone.
Risk assessment demonstrates how deep contextual knowledge enhances prompt design. Financial advisors develop prompts that evaluate investment opportunities, incorporating factors like market trends, regulatory updates, and client risk preferences. These prompts go beyond technical evaluations, addressing the nuanced factors that matter most to decision-making.
What ties all these use cases together is the domain expert’s understanding of the real-world consequences of errors. They know which mistakes are minor and which could lead to regulatory violations, safety risks, or damaged client relationships. This awareness ensures that prompt refinements are laser-focused on what truly matters, delivering results that generic approaches simply can’t achieve.
Conclusion
Key Takeaways for Domain Experts
Mastering prompt engineering comes from hands-on practice and continuous learning. Successful domain experts treat it as a step-by-step process, starting with simple prompts and refining them over time through practical use and experimentation.
Collaboration in workshops and labs plays a crucial role in improving prompt quality. When domain experts team up with engineers in structured settings, they bridge the gap between technical possibilities and the specific needs of their field. This teamwork ensures prompts are not only technically sound but also tailored to address the unique challenges that only domain specialists can identify.
Iterative improvements and thorough documentation are key to achieving success in prompt engineering. For example, in June 2023, OpenAI's GPT-4 team partnered with medical professionals from Johns Hopkins University. During this collaboration, oncologists reviewed and fine-tuned more than 10,000 medical prompts, resulting in a 28% increase in accuracy for cancer-related queries. This kind of approach highlights the value of ongoing refinement and sets a benchmark for effective practices.
How Latitude Supports Prompt Engineering
Latitude provides the tools needed for seamless collaboration between domain experts and engineers. Its open-source platform allows teams to tailor their prompt engineering workflows to meet the specific demands of their fields.
The platform offers real-time feedback and version control, enabling domain experts to track changes and evaluate improvements systematically. Instead of relying on chaotic trial-and-error methods, Latitude turns the process into a structured, measurable system of experimentation.
Latitude also supports the continuous learning that underpins effective prompt engineering. With automated validation workflows and robust documentation tools, domain experts can maintain the iterative cycles needed to adapt prompts as requirements change. By connecting domain knowledge with technical execution, Latitude ensures that expert insights are transformed into reliable, production-ready features for large language models. This approach embodies the collaborative and practical mindset essential to successful prompt engineering.
FAQs
How can domain experts without technical skills work effectively with engineers on prompt engineering?
Domain experts who may not have a technical background can still make a big impact in prompt engineering by leaning into their strengths - deep subject knowledge and effective communication. The process starts with clearly outlining specific goals, providing relevant context, and defining desired outcomes. This ensures that both the experts and engineers are on the same page from the beginning.
Active involvement is essential. Participate in testing and reviewing cycles, offering feedback to fine-tune prompts and enhance AI results. While coding skills aren't necessary, understanding basic prompt design principles - like setting clear objectives and maintaining context - can make collaboration smoother and more productive. When domain expertise meets engineering know-how, the result is often more precise and relevant AI-driven solutions.
What are common mistakes domain experts make when creating prompts for LLMs, and how can they avoid them?
One mistake people often make is crafting vague or overly broad prompts, which can lead to responses that miss the mark or feel off-topic. To sidestep this, aim for prompts that are clear, specific, and packed with enough context to help the AI fully grasp what you’re looking for.
Another common misstep is skipping the refinement process. Start with a rough draft, test it out, and tweak the wording or structure based on the results. Using precise, purpose-driven language and trying out different phrasings can go a long way in improving the relevance and accuracy of the AI's responses.
By prioritizing clarity, providing context, and refining prompts through trial and error, experts in any field can create prompts that drive better, more actionable results.
What is the 'temperature setting' in prompt engineering, and how does it affect the model's output?
The temperature setting in prompt engineering plays a key role in shaping how the AI responds. A lower temperature (like 0.1 to 0.5) pushes the AI toward more predictable and precise answers. This makes it perfect for tasks that demand accuracy, such as summarizing information or tackling factual questions. On the flip side, a higher temperature (above 0.8) allows the AI to produce more varied and creative responses, which works well for brainstorming sessions or generating imaginative content.
The best approach? Match the temperature to your goal. Stick with lower settings for consistent, reliable results, and turn it up when you’re looking for fresh, diverse ideas.