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This is a 60-part series on building AI systems that survive real users. It is written in order, but usable in any order.
It runs from what a language model actually is, through retrieval and RAG, into agents and multi-agent systems, and ends at the operational layer: tracing, evaluation, security and governance. Each post is a short read built around one production decision.
New here? Start at Day 1 and go in sequence. Solving something specific? Jump to the chapter that matches your problem. Each post links to the next, so you can keep reading from wherever you land.
AI Systems Basics
Days 1–6. Why the model is the smallest part of the problem.
- Day 1: Why model choice is rarely the first production AI problem
- Day 2: Why LLMs feel intelligent but still generate one step at a time
- Day 3: Why tokenization quietly affects cost, limits, and reliability
- Day 4: Why more context can make an AI system worse
- Day 5: Why vague prompts become vague systems
- Day 6: Why creativity settings are product decisions
From LLM Demo to AI Product
Days 7–12. What separates something that demos well from something people can rely on.
- Day 7: Why confident AI answers still need evidence
- Day 8: Why production AI needs structured outputs
- Day 9: Why a capable model is still not a product
- Day 10: When to use a chatbot, workflow, or agent
- Day 11: Why RAG is an evidence design problem, not a buzzword
- Day 12: Why retrieval quality starts before search
Retrieval Foundations
Days 13–18. How chunking, embeddings and search decide what evidence your system can find at all.
- Day 13: Why chunk boundaries shape answer quality
- Day 14: Why embeddings are useful and easy to overtrust
- Day 15: Why vector stores are infrastructure, not magic memory
- Day 16: Why keyword search and semantic search both matter
- Day 17: Why hybrid search is often the practical default
- Day 18: Why relevant data can still be unauthorized data
Evidence-Driven RAG Patterns
Days 19–24. Making answers traceable: reranking, citations, and breaking hard questions apart.
- Day 19: Why reranking is where retrieval becomes useful
- Day 20: Why every important AI claim needs provenance
- Day 21: Why RAG needs both broad context and precise evidence
- Day 22: Why one user question may need many searches
- Day 23: Why complex AI questions need decomposition
- Day 24: Why some answers live in relationships, not documents
Production RAG Operations
Days 25–30. Live data, freshness, recovery from weak evidence, and measuring retrieval separately from generation.
- Day 25: Why production knowledge often lives in systems, not PDFs
- Day 26: Why evidence is becoming multimodal
- Day 27: Why freshness is part of correctness
- Day 28: Why weak evidence should trigger recovery, not confidence
- Day 29: Why reflection only matters when tied to evidence
- Day 30: Why RAG must be evaluated in parts
Agentic AI Foundations
Days 31–36. Goals, loops, planning, tools and memory. What actually makes an agent an agent.
- Day 31: Why agents need outcomes, boundaries, and stop conditions
- Day 32: Why agent behavior is a loop, not a single prompt
- Day 33: Why planning reduces wasted AI actions
- Day 34: Why tool access is where AI becomes operational risk
- Day 35: Why agent memory is not one database
- Day 36: Why reflection should change the next action
Agent Control and Supervision
Days 37–42. Human checkpoints, guardrails, recovery design and budgets.
- Day 37: Where human review actually belongs in AI workflows
- Day 38: Why guardrails should enable safe action
- Day 39: Why production agents need recovery design
- Day 40: Why autonomy needs budgets
- Day 41: Why one giant agent is rarely the cleanest design
- Day 42: Why supervisor agents help and where they bottleneck
Multi-Agent Design Patterns
Days 43–48. Supervisors, hierarchies, pipelines, peers, shared state and debate.
- Day 43: When hierarchy helps multi-agent work scale
- Day 44: Why dynamic dispatch needs ownership rules
- Day 45: Why pipelines make AI handoffs inspectable
- Day 46: Why peer agents need protocols, not vibes
- Day 47: Why shared state needs structure and attribution
- Day 48: Why AI debate needs a reliable judge
Agent Coordination Contracts
Days 49–54. Roles, protocols, handoffs, shared memory and deciding who wins a disagreement.
- Day 49: When decentralized agent behavior makes sense
- Day 50: Why role design matters more than agent count
- Day 51: Why agents need a shared language
- Day 52: Why shared memory becomes a permission problem
- Day 53: Why handoffs need contracts
- Day 54: Why multi-agent systems need decision rules
Production AI Readiness
Days 55–60. Tracing, system-level evaluation, prompt injection, governance, and the whole picture.
- Day 55: Why observability is not optional for AI systems
- Day 56: Why evaluating the model is not enough
- Day 57: Why retrieved content must stay untrusted
- Day 58: Why governance belongs in the architecture
- Day 59: Why production AI is coordinated infrastructure
- Day 60: From model demos to mission-ready AI systems
Written by Vamsi Krishna Annamreddy. If a post changes how you build something, I would genuinely like to hear about it.