Overview

If you’re building an application powered by a Large Language Model (LLM), you’ve probably faced the daunting question: which model should I use, and how should I prompt it? With options ranging from GPT-4 to PaLM2 and endless ways to structure prompts, the choices can feel overwhelming. But here’s the good news: finding the right model and prompt isn’t magic—it’s a process.

In this blog, we’ll walk you through how to choose the best model and craft effective prompts for your specific use case. Let’s break it down into simple, actionable steps.

Step 1: Understand Your Use Case

Before diving into models and prompts, take a step back. What are you trying to achieve? Your use case will heavily influence your choices.

For example:

• Summarization Tool: You need a model that excels at coherence and coverage, with prompts that guide it to produce concise summaries.

• Customer Support Chatbot: You might prioritize relevance and engagement, with prompts that encourage dynamic, friendly responses.

• Data Extraction: Accuracy is king, and your prompts should focus on precision and avoiding hallucinations.

Pro Tip: List your top priorities (e.g., accuracy, speed, cost) to guide your decisions.

Step 2: Choose the Right Model

Not all LLMs are created equal. Here’s a quick cheat sheet:

1. GPT-4

   • Strengths: Best for complex, nuanced tasks requiring detailed reasoning.

   • Trade-Off: Higher cost and slightly slower responses.

   • Use Case: Ideal for legal or medical summaries, in-depth customer interactions.

2. PaLM2

   • Strengths: Great for multilingual tasks and general-purpose applications.

   • Trade-Off: Slightly less robust for reasoning-heavy tasks compared to GPT-4.

   • Use Case: Perfect for global applications like translation tools.

3. Smaller Models (e.g., GPT-3.5 or Open-Source Models)

   • Strengths: Cost-efficient and faster for simpler tasks.

   • Trade-Off: Lower performance on nuanced or domain-specific tasks.

   • Use Case: Good for tasks like FAQs or basic text classification.

Pro Tip: Start with the smallest model that meets your baseline needs and scale up if required.

Step 3: Craft Effective Prompts

Once you’ve picked a model, the next challenge is getting it to do what you want. A well-crafted prompt can make all the difference. Here’s how:

1. Start Simple, Then Iterate

   Begin with a straightforward prompt to test the model’s baseline performance.

   Example for Summarization:

   Summarize the following text: "..."

2. Add Context and Instructions

   If the output isn’t quite right, provide more detail.

   Example:

   Summarize the following text in 3-4 sentences, focusing on key points without adding opinions: "..."

3. Test Variations

   Try different styles to see what works best.

   • Role-based prompt

     You are an expert editor. Summarize this text concisely and professionally: "..."

   • Question-based prompt

     What are the main points of this text? Summarize in 3 sentences

4. Use Few-Shot Examples

   For complex tasks, show the model what you expect by including examples in the prompt.

   Example:

   Here’s how summaries should look:  
   - Text: "Example 1" → Summary: "Example summary 1."  
   - Text: "Example 2" → Summary: "Example summary 2."  
   Now summarize: "..."

Pro Tip: Always test multiple variations and measure performance against your metrics (e.g., accuracy, relevance).

Step 4: Balance Trade-Offs

Choosing the best model and prompt often involves balancing trade-offs:

• Cost vs. Performance:

  Smaller models or simpler prompts reduce costs but may compromise quality.

• Speed vs. Depth:

  Detailed prompts can improve accuracy but increase response time.

Pro Tip: Use simpler models for high-frequency tasks and reserve advanced models for critical workflows.

Step 5: Iterate and Refine

The first combination of model and prompt you try is rarely the best. Treat this process as iterative:

1. Analyze outputs against your evaluation metrics.

2. Adjust the prompt structure, adding or removing instructions.

3. Test alternative models if your chosen one doesn’t meet expectations.

Example Workflow:

1. Start with GPT-3.5 for speed.

2. If outputs lack depth, switch to GPT-4 with a refined prompt.

3. If costs spike, simplify the task or use GPT-3.5 for parts of the pipeline.

Tools to Help You Experiment

Here are some tools to streamline your process:

1. OpenAI Playground

   • Experiment with different prompts and models interactively.

   • Adjust parameters like temperature and max tokens.

2. LangChain

   • Build pipelines to test multiple prompts and models in parallel.

3. Evaluation Frameworks

   • Use tools like BLEU or ROUGE to evaluate text-based outputs quantitatively.

4. Human Feedback Loops

   • Gather user feedback to fine-tune both the model and prompts.

Future AGI's Experiment Feature to Simplify the Process

Here’s where Future AGI’s Experiment feature can transform your workflow. Instead of manually testing models, prompts, and evaluation criteria, our platform lets you handle it all in one place.

Here’s what makes it powerful:

1. Define Multiple Prompts: Test various prompt styles—detailed, simple, few-shot—without the manual effort.

   

2. Choose from a Large Suite of Models: Compare performance across models like GPT-4, PaLM2, and others to find the best fit for your use case.

3. Custom Evaluations: Define the metrics that matter to you—accuracy, relevance, latency, or even domain-specific measures.

   

4. Comprehensive Dashboard: View side-by-side comparisons of models, prompts, and evaluation metrics in an intuitive dashboard.

5. Winner Recommendation: Our system analyzes your criteria and helps you pick the best-performing model and prompt combination.

   

Example: If you’re building a summarization app, you can set up prompts for detailed and concise summaries, test them on models like GPT-3.5 and GPT-4, and evaluate them for coherence, coverage, and latency—all in one streamlined process.

With FutureAGI, the trial-and-error phase becomes a fast, data-driven experiment, saving you time and delivering insights that are impossible to gather manually.

Learn how HRs can use the Experiment feature in their workflow to boost their productivity in this blog.

Summary

Choosing the best model and prompt for your use case doesn’t have to be overwhelming. Start by understanding your product’s goals, test models that align with those needs, and experiment with prompts to refine results. Remember, this isn’t a one-time process. As your use case evolves, your choice of model and prompt might too. By continuously iterating, you’ll not only find the best solution for today but also set the foundation for building smarter, more adaptive AI systems in the future.

So, don’t be afraid to experiment—your perfect model and prompt are just a few iterations away!