production
14 results
AI Agent Memory Architectures: The Missing Piece in Most Agent Builds
Why most agents feel dumb after turn one — and how to fix it with mem0, pgvector, and the right memory architecture for your use case.
AI Agent Observability: How to Monitor Agents in Production
Monitor AI agents in production with LangSmith tracing, structured logging, and alert patterns that catch real failures before your users do.
LLM Model Routing: Pick the Right Model for Every Task and Cut Costs 80%
Route LLM queries across nano, mid, and frontier tiers using LiteLLM and aicredits.in — same output quality, 80% lower API spend on mixed workloads.
Anthropic Batch API: Cut Your AI Costs 50% for High-Volume Workloads
Anthropic's Message Batches API processes async workloads at 50% off standard pricing. Complete Python implementation, hybrid architecture patterns, and failure handling.
Long-Context Prompting: How to Use 200K+ Token Windows Without Losing Quality
200K token windows degrade in the middle. Learn anchoring, explicit referencing, and hierarchical summarization strategies to get reliable results at scale.
Prompt Versioning: Treat Your Prompts Like Production Code
How to version, diff, A/B test, and roll back prompts in production using Git, PromptLayer, and LangSmith — before a silent regression tanks your metrics.
Prompt Injection Defense for Production AI Systems
Beyond the basics — how to defend your production AI application against real prompt injection attacks with input sanitization, sandboxing, and output validation.
10 Vibe Coding Anti-Patterns That Will Bite You in Production
Vibe coding is fast but these 10 patterns quietly build time bombs — real mistakes I've seen break AI-assisted apps when they hit real users.
Prompt Engineering for RAG Pipelines: How to Write Queries That Actually Retrieve the Right Context
Retrieval-Augmented Generation lives or dies on query quality. Most teams get the retrieval wrong, not the generation.
Prompt Caching: How to Cut AI API Costs by 80% (Anthropic + OpenAI)
A practical guide to prompt caching on Anthropic and OpenAI APIs — how it works, what it saves, and the patterns that maximize cache hit rates in production.
Agentic RAG — Moving Beyond Simple Q&A
Simple RAG retrieves once and answers. Agentic RAG lets the model decide what to retrieve, when, and how many times — here's how it works and when to use it.
AI Agent Evaluation: How to Know If Your Agent Actually Works
Move beyond vibes-based testing — build a proper eval framework for AI agents covering task completion, hallucination rate, latency, and cost with real tooling recommendations.
Build a Customer Support AI Agent That Doesn't Hallucinate
How to architect a grounded AI support agent using RAG, strict system prompt rules, and adversarial testing — so it never makes up answers about your product.
