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AI Launch Playbook

An interactive checkpoints guide for deploying AI features securely and cost-effectively.

Reliability

Implement Fallback Models

API providers go down. Ensure you have fallback models configured to catch inference errors.

RecommendationUse a primary model (e.g. Claude 3.5 Sonnet) and catch API exceptions by immediately calling a secondary provider (e.g. GPT-4o-mini).
Reliability

Exponential Backoff Retries

Rate limits (HTTP 429) happen under high usage. Retrying immediately will worsen the load.

RecommendationImplement a retry queue with exponential backoff (e.g., retry after 1s, 2s, 4s, 8s) with random jitter.
Security

Validate Input Guardrails

Guard against prompt injections where malicious inputs hijack LLM behavior.

RecommendationUse lightweight classifier models (like LlamaGuard) or enforce strict system schemas to filter user inputs before LLM pass-through.
Security

PII Data Scrubbing

Avoid sending Personally Identifiable Information (PII) to public model APIs to maintain compliance.

RecommendationStrip phone numbers, emails, and credit card numbers from user queries using regex or Presidio before sending them to the API.
Cost

Enable Prompt Caching

Repeating long context inputs (like doc archives or system instructions) repeatedly gets expensive.

RecommendationUtilize models that support prompt caching (like Anthropic Claude or DeepSeek) to reduce pricing for cached context by up to 90%.
Cost

Semantic Routing

Using complex flagship models for simple classifications or routing is a waste of budget.

RecommendationRoute simple queries (e.g., greetings, yes/no queries) to smaller models (GPT-4o-mini, Phi-4) and escalate complex tasks to frontier models.
Performance

Stream Responses (TTFT)

Waiting for full token generation takes seconds and hurts user experience.

RecommendationImplement SSE (Server-Sent Events) to stream tokens directly to the UI. Focus on optimizing Time-To-First-Token (TTFT) metrics.
Performance

Semantic Caching

Executing repetitive queries hits external APIs, resulting in latency and costs.

RecommendationUse a vector cache (like Redis or GPTCache) to store and retrieve replies for semantically identical questions.
Monitoring

Log Token Tracing & Costing

Without logging, you cannot track model usage, token consumption, or cost distribution.

RecommendationIntegrate open-source LLM tracing frameworks (like LangFuse, Phoenix, or Arize) to visualize execution traces and pricing.