Choose the Right AI Model for Your Prompt Template

Model Selection Principles

Task specificity matters most. Different models excel at different tasks. What works for generating creative sales emails might not work for extracting structured data from cases. Always test models on your actual use case, not general benchmarks.

Performance over recency. A newer model isn't automatically better for your needs. Don't default to the newest model — measure performance on your task.

Cost vs. accuracy trade-offs. More expensive models often (but not always) produce higher quality output. Match model cost to response visibility and business impact.

Consistency is critical for production. A model that produces excellent responses 80% of the time and poor responses 20% of the time is not ready for production. Test each model multiple times with the same inputs to measure consistency, not just peak performance.

Test and Select a Model

Follow this process to make evidence-based model choices.

Step 1: Define success criteria. Before testing any models, decide what "good" means for your use case. Identify 2–3 measurable criteria. For example, for a sales email prompt: personalization (email references specific account details), tone (professional but conversational, scored 1–5), and length (100–150 words).

Step 2: Create test data. Prepare 10–20 representative examples from real-world usage. Use actual Salesforce records, not made-up test data. Real data surfaces edge cases such as empty fields, unusual values, very long or short text, and special characters. Include variety across industries, case types, and account sizes, and include both typical records and records that have been problematic.

Step 3: Test each model. Run your prompt template against each available model using your test data. Use the exact same prompt template and test data for all models — only change the model selection.

Step 4: Measure results. Score each output against your success criteria. Calculate summary statistics such as pass rate, average score, and compliance rate across all test records.

Step 5: Analyze the data. Compare models across your success criteria. Identify which model performs best on the criteria that matter most for your use case.

Step 6: Check consistency. For the top 2–3 models, run the same prompt five times against 5 test records (25 total runs per model). High variance means the model is unreliable for production. Choose models with low variance.

Step 7: Choose based on evidence. Select the model that performed best on your actual success criteria, not on reputation or marketing claims. Document your choice, including which criteria it won on, the test results summary, and the date of testing. As new models become available, you can re-test and compare against your baseline.

Know When to Switch Models

Your model choice isn't permanent. Consider switching in these situations.

• New models become available. When Salesforce releases new models, test them against your baseline using the same process. Don't assume new equals better.
• Your use case changes. If requirements change — for example, you now need multi-language support or longer output — retest models.
• Performance degrades. If you notice output quality declining over time, test alternative models. Sometimes model updates affect performance for specific use cases.
• Cost considerations change. If your volume increases dramatically, a more cost-effective model might make sense even if quality drops slightly. Analyze whether the quality difference is noticeable to end users and whether it affects business outcomes.

Common Model Selection Mistakes

• Choosing based on general benchmarks. Benchmarks measure general capabilities. Your specific task might not align with benchmark tasks. Always test with your actual prompt and data.
• Never retesting. Models change, new models are released, and your use case evolves. Schedule quarterly model reviews for critical prompts.
• Testing with artificial data. Production data is messy. Real records have empty fields, edge cases, and unexpected values. Always test with real Salesforce records.
• Testing once. Single tests don't reveal consistency issues. Test multiple times with the same inputs to measure consistency.
• Optimizing for the wrong metric. Define success criteria first, then test for those specific criteria — not for general quality or creativity.

Model Comparison Checklist

When comparing models, evaluate these items.

• Accuracy: Does output match expected content and format?
• Consistency: Do repeated runs produce similar quality?
• Compliance: Does response fit field size and format constraints?
• Tone: Does voice match your brand and use case?
• Completeness: Are all required elements included?
• Speed: Does response time meet user expectations?
• Cost: Does price align with business value of this use case?
• Edge cases: How does it handle unusual or minimal data?