Prototype to Production

Transitioning a prototype to production requires careful planning and execution. This checklist highlights critical steps, drawing from our flagship use cases, to ensure your deployment is robust, efficient, and meets business goals.

🗂️ TL;DR Matrix

Checklist Area	Key Focus / Actions	Why it Matters
Define Success Criteria	• Define measurable KPIs & SLOs (accuracy, cost, latency) • Ensure targets are measurable via logs	Provides clear targets; proves value
Document Model Rationale	• Select initial models deliberately based on trade-offs • Document the “why” behind model choices	Justifies choices; aids future updates
Robust Evaluation & Testing	• Build automated tests (“eval suite”) using a golden set • Focus on factuality, hallucinations, tool errors • Test tool reliability & edge cases	Ensures quality; prevents regressions before release
Observability & Cost	• Implement essential logging for monitoring & debugging • Set cost guardrails (token limits, usage modes)	Enables tuning; keeps spending within budget
Safety & Compliance	• Use safety mechanisms (moderation APIs, prompts) • Enforce domain-specific compliance rules • Mandate Human-in-the-Loop (HITL) for high-risk outputs	Ensures responsible operation; meets requirements
Model Updates & Versioning	• Define version pinning strategy • Implement A/B testing for new versions • Create rollback procedures	Maintains stability while allowing improvements

1. Define Success Criteria Quantitatively: Move beyond “it works” to measurable targets before major development.

   • Set Key Performance Indicators (KPIs) & SLOs: Define specific targets for business value (e.g., RAG accuracy > 95%, OCR cost < $X/page) and performance (e.g., P95 latency < 1s, error rates).

   • Ensure Measurability: Confirm that all KPIs and SLOs can be directly measured from system logs (e.g., tracking total_tokens, critique_status).

2. Document Initial Model Selection Rationale: Justify your starting model choices for future reference.

   • Choose Models Deliberately: Use the Model-Intro Matrix and use cases to select appropriate models for each task (e.g., o4-mini for speed/cost, gpt-4.1 for accuracy, o3 for depth).

   • Record the “Why”: Briefly document the reasoning behind your choices (cost, latency, capability trade-offs) in code comments or design docs so future teams understand the context.

3. Implement Robust Evaluation & Testing: Verify quality and prevent regressions before shipping changes.

   • Build an Automated Eval Suite: Create a repeatable test process using a “golden set” (50-100 diverse, expert-verified examples). Focus tests on factuality, hallucination rate, tool-error rate, and task-specific metrics.

   • Test Reliably: Rigorously test integrated tool reliability (success rate, error handling) and system behavior under load and with edge cases (malformed data, adversarial inputs).

4. Establish Observability & Cost Controls: Monitor performance and keep spending within budget.

   • Set Cost Guardrails: Prevent unexpected cost increases by defining max token limits per stage and considering operational modes (“Fast,” “Standard,” “Thorough”) to balance cost and performance.

   • Implement Essential Logging: Capture key operational data via structured logs for each processing stage to enable debugging and monitoring.

5. Implement Safety & Compliance Guardrails: Ensure responsible operation and meet requirements.

   • Use Safety Mechanisms: Employ tools like OpenAI’s moderation APIs, safety-focused system prompts, or sentinel models for checks, especially with user input or sensitive topics.

   • Enforce Compliance: Build in checks relevant to your specific industry and risks (e.g., legal constraints, lab safety).

   • Require Human-in-the-Loop (HITL): Mandate human review for low-confidence outputs, high-risk scenarios, or critical decisions, ensuring the workflow flags these items clearly.

6. Manage Model Updates and Versioning: Prepare for model evolution over time.

   • Version Pinning Strategy: Decide whether to pin to specific model versions for stability or automatically adopt new versions for improvements.

   • A/B Testing Framework: Establish a process to evaluate new model versions against your key metrics before full deployment.

   • Rollback Plan: Create a clear procedure for reverting to previous model versions if issues arise with updates.

   • Monitor Version Performance: Track metrics across model versions to identify performance trends and inform future selection decisions.

Adaptation Decision Tree

Communicating Model Selection to Non-Technical Stakeholders

When explaining your model choices to business stakeholders, focus on these key points:

• Align with Business Outcomes: Explain how your model selection directly supports specific business goals (time savings, cost reduction, improved accuracy).

• Translate Technical Metrics: Convert technical considerations into business impact:

  • “This model reduces processing time from 5 seconds to 0.7 seconds, allowing us to handle customer inquiries 7x faster”

  • “By using the mini variant, we can process 5x more documents within the same budget”

• Highlight Trade-offs: Present clear scenarios for different models:

  • “Option A (GPT-4.1): Highest accuracy but higher cost - ideal for client-facing legal analysis”

  • “Option B (GPT-4.1 mini): 90% of the accuracy at 30% of the cost - perfect for internal document processing”

• Use Concrete Examples: Demonstrate the practical difference in outputs between models to illustrate the value proposition of each option.

Price and Utility Table (Apr 2025)

Model	Context Window	Input Price (per 1M tokens)	Output Price (per 1M tokens)	Best For
GPT-4.1	1M	$2.00	$8.00	Long-doc analytics, code review
GPT-4.1 mini	1M	$0.40	$1.60	Production agents, balanced cost/performance
GPT-4.1 nano	1M	$0.10	$0.40	High-throughput, cost-sensitive applications
GPT-4o	128K	$5.00	$15.00	Real-time voice/vision chat
GPT-4o mini	128K	$0.15	$0.60	Vision tasks, rapid analytics
o3 (low)	200K	$10.00*	$40.00*	Bulk triage, catalog enrichment
o3 (med)	200K	$10.00*	$40.00*	Knowledge base Q&A
o3 (high)	200K	$10.00*	$40.00*	Multi-step reasoning, troubleshooting
o4-mini (low)	200K	$1.10*	$4.40*	Vision tasks, rapid analytics
o4-mini (med)	200K	$1.10*	$4.40*	Balanced vision + reasoning
o4-mini (high)	200K	$1.10*	$4.40*	Deep reasoning with cost control

Note: The low/med/high settings affect token usage rather than base pricing. Higher settings may use more tokens for deeper reasoning, increasing per-request cost and latency.

Prompt-pattern Quick Sheet (Token vs Latency Deltas)

Prompt Pattern	Description	Token Impact	Latency Impact	Best Model Fit
Self-Critique	Ask model to evaluate its own answer before finalizing	+20-30% tokens	+15-25% latency	GPT-4.1, o3
Chain-of-Thought (CoT)	Explicitly instruct to “think step by step”	+40-80% tokens	+30-50% latency	o3, o4-mini (high)
Structured Outputs	Use JSON schema or pydantic models for consistent formatting	+5-10% tokens	+5-10% latency	All models
Zero-Token Memory	Store context in external DB rather than in conversation	-70-90% tokens	-5-10% latency	GPT-4.1 family
Skeleton-Fill-In	Provide template structure for model to complete	-10-20% tokens	-5-15% latency	o4-mini, GPT-4.1 nano
Self-Consistency	Generate multiple answers and select most consistent	+200-300% tokens	+150-250% latency	o3 (high)
Role-Playing	Assign specific personas to model for specialized knowledge	+5-15% tokens	Neutral	GPT-4o, o4-mini
Tournament Ranking	Compare options pairwise rather than scoring individually	+50-100% tokens	+30-60% latency	o3, o4-mini (high)
Tool-Calling Reflex	Prompt model to call tools when uncertainty is detected	+10-30% tokens	+20-40% latency	o3, GPT-4.1