Effective AI Utilization — Table of Contents

A comprehensive guide to building production AI systems, drawn from patterns observed in BrianBot and generalized into reusable principles. Each memo below covers a discrete domain; together they form a complete playbook.

Core Pillars

1. Model Routing Strategies — How to select the right model for the right task, from static config to dynamic routing. Covers provider abstraction, the routing decision tree, and multi-provider architectures.

1. Token Optimization Playbook — Managing context windows, controlling costs, and getting more out of every token. Covers counting, budgeting, compression, caching, and cost tracking.

Supporting Concepts

1. Model Fallback and Resilience — What happens when your primary model fails. Retry logic, fallback chains, circuit breakers, and graceful degradation patterns.

1. Temperature and Parameter Tuning — When to use 0.3 vs 0.7 vs 1.0, and how parameter choices map to task types (extraction, generation, analysis, creative).

1. Prompt Architecture — Designing override hierarchies, system prompt management, and the separation of instruction from content.

1. AI Pipeline Design — Sequencing multiple AI calls into a coherent production pipeline. Dependencies, parallelism, and state management between steps.

1. Cost Tracking and Budget Controls — From token counting to dollar estimates. Building visibility into AI spend and setting guardrails.

1. Queue and Rate Limiting for AI Workloads — Managing concurrency, respecting API rate limits, and designing job queues that don't blow your budget or get throttled.

1. Context Window Management — Strategies for working within token limits: rolling windows, summarization, chunking, and priority-based context assembly.

1. Multi-Provider Strategy — Why and how to integrate multiple AI providers (Anthropic, OpenAI, Google). Key management, capability mapping, and avoiding vendor lock-in.

1. Streaming vs Blocking AI Calls — When to stream responses and when to await them. Tradeoffs for UX, pipeline design, and error handling.

1. AI Observability and Debugging — Logging, tracing, and monitoring AI calls in production. Making failures visible and diagnosable.

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