GuideAdvanced
Cost Optimization & Caching Guide
Master cost reduction for AI systems: understand cost drivers (tokens, API calls, embeddings), implement Redis caching and semantic caching (similar queries = cached responses using embeddings), apply prompt optimization and model selection strategies, and build a system that achieves 50-80% cost reduction with real numbers. The only guide in Spanish that covers semantic caching hands-on.
- 64
- lessons
- 8
- modules
- English · Spanish
- available in
- Yes
- certificate
- Free
- access
Outcomes
What you'll be able to do
- Break down AI costs by component: tokens, embeddings, API calls, storage
- Integrate cost tracking with monitoring dashboards and set budget alerts
- Optimize prompts to reduce tokens 30-50% without sacrificing quality
- Set up Redis and implement exact-match response caching
- Apply cache-aside and write-through patterns for LLM responses
- Build semantic cache: similar queries = cached responses using embeddings and cosine similarity
- Use RediSearch for vector similarity search in Redis
- Implement model selection: when to use GPT-4 vs GPT-3.5 vs local models based on query complexity
- Apply batching and tiered cost optimization strategies
- Build a cost-reduced AI system demonstrating 50-80% savings with real benchmarks
Before you start
What you need to bring
It's for you if...
- AI Engineers who completed Monitoring & Observability (#18) and want to reduce costs, not just measure them
- Teams with limited AI budget scaling LLM usage who need 50-80% cost reduction
- Startups and enterprises justifying AI ROI with documented savings
- Developers who want to differentiate with semantic caching skills (rare in Spanish market)
- Tech leads preparing cost-optimized AI systems for production
Requirements and materials
- Python intermediate (OOP, async, type hints, Pydantic basics)
- Experience with REST APIs (FastAPI or similar)
- Basic LLM usage (OpenAI API or equivalent)
- Monitoring concepts from Guide #18 (metrics, dashboards, cost tracking)
- Basic familiarity with embeddings (from previous path guides)
Content
The syllabus, module by module
Open any of them to see its lessons.
- Introduction to Prompt Optimization for Cost
- System Prompt Optimization
- Efficient User Prompt Templates
- Few-Shot Examples — The Effective Minimum
- Context Window Optimization — Stop Paying for Wasted Context
- Quality Validation After Optimization
- Automated Optimization Pipeline
- 8. Project: Prompt Optimization Pipeline
- Introduction to Redis Fundamentals for AI
- Redis Setup and Installation
- Basic Operations with redis-py
- Key Design for LLM Caching
- TTL and Expiration Strategies for LLM Responses
- Serializing LLM Responses for Redis
- Error Handling and Fallback Patterns for LLM Caching
- 8. Project: Redis Caching Layer for LLM Responses
Where it fits
This guide is part of something bigger
It's studied inside these programs, with support and dates.
Common questions
What people usually ask
No limit. It's a free guide: come in whenever you like, as often as you like.
No. Modules run from easier to harder, but you can jump to the one you need. Progress is saved per lesson.
Whatever is needed is listed under “What you need to bring”, above. If nothing is listed there, you can start from zero.
In the Club's WhatsApp group, and every two weeks there's a live with an instructor where questions get worked through.
Yes. It's issued automatically once you finish every lesson, with a verifiable code you can share on LinkedIn.
No. This guide is self-paced with no dates. The bootcamp is live, by cohort, with work someone reviews.
Start whenever you like
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