Evaluate AI Models

This guide walks you through a systematic approach to evaluating AI models for production deployment using Lattice.

Overview

Model evaluation involves:

Defining your requirements
Gathering relevant documentation
Comparing capabilities and limitations
Testing with your specific use cases
Making a data-driven decision

Step 1: Define Your Requirements

Before evaluating models, clarify what matters most:

Latency: What response time is acceptable?
Throughput: How many requests per second?
Context window: How much input do you need to process?

Create a scenario in Lattice to capture these requirements:

name: Model Evaluation - RAG Chatbot
workload_type: rag
traffic_profile: medium_volume
slo_requirements:
  p95_latency_ms: 1000
  throughput_rps: 50
budget:
  monthly_limit_usd: 3000

Step 2: Gather Documentation

Apply relevant blueprints to populate your workspace:

Go to Blueprints and apply vendor blueprints for models you’re considering
Add any additional sources (benchmarks, blog posts, comparison articles)
Wait for indexing to complete

For a Claude vs GPT-4 evaluation, you might apply:

Anthropic Claude Blueprint
OpenAI GPT-4 Blueprint
Add recent benchmark URLs manually

Step 3: Generate a Comparison Matrix

Ask Lattice to create a structured comparison:

Create a detailed comparison table of Claude Sonnet and GPT-4 Turbo
for RAG applications. Include:
- Context window size
- Input/output pricing
- Latency benchmarks
- Key strengths
- Known limitations

Save the generated table to Studio for reference.

Step 4: Evaluate Against Your Scenario

With your scenario active, ask targeted questions:

Given my RAG chatbot scenario with P95 latency under 1000ms
and $3000/month budget, which model is the better choice?
Show your reasoning with citations.

Lattice will consider your specific constraints when recommending.

Step 5: Deep-Dive on Specific Concerns

Investigate areas that matter most to your use case:

For Latency-Sensitive Applications

What are the typical first-token and total response latencies
for Claude Sonnet vs GPT-4 Turbo? Include any regional variations.

For Cost Optimization

Calculate the monthly cost for each model assuming:
- 1 million input tokens/day
- 200K output tokens/day
- 30 days
Include prompt caching benefits if available.

For Quality Requirements

Compare Claude Sonnet and GPT-4 Turbo on:
- Instruction following accuracy
- Factual grounding
- Handling of ambiguous queries
Cite relevant benchmarks and studies.

Step 6: Document Your Decision

Generate an executive memo summarizing your evaluation:

Write an executive memo recommending a model choice for our
RAG chatbot project. Include:
- Summary of options evaluated
- Key decision criteria
- Recommended choice with rationale
- Cost projections
- Risk considerations

Save this artifact to Studio as your decision record.

Example Evaluation Output

Here’s what a typical evaluation artifact might look like:

Criterion	Claude Sonnet	GPT-4 Turbo	Winner
Context Window	200K tokens	128K tokens	Claude
Input Price	$3/1M tokens	$10/1M tokens	Claude
Output Price	$15/1M tokens	$30/1M tokens	Claude
P95 Latency	~800ms	~1200ms	Claude
Reasoning	Excellent	Excellent	Tie
Function Calling	Good	Excellent	GPT-4
Safety	Excellent	Good	Claude

Best Practices

Next Steps

Compare Providers — Evaluate cloud infrastructure options
Configure Scenarios — Refine your workload requirements
Build Stacks — Generate deployment configurations