📰 Dev.to · Abdul Rehman

Architecture decisions, cost management, and error handling for LLM pipelines at scale. Real lessons from a production job board platform.

Why your AI agent works perfectly in demos then fails in production. Real patterns for debugging LLM output, system prompts, and building reliable agent loops.

Practical techniques for testing and validating LLM outputs in production, drawn from real shipping experience with AI-powered job boards and document tools.

How logging, cost tracking, and error monitoring turned a fragile LLM pipeline into a production system processing 10k jobs daily.

Why observability beats prompt engineering for production LLM systems. Real lessons from a 1M+ listing AI pipeline.

The architecture, cost strategies, and hard-won lessons from a production LLM scoring pipeline running at scale.

How an AI-generated function passed every test but failed under production load, and how to build systems that catch these bugs.

The production architecture decisions behind a Next.js + OpenAI job board handling 10K+ daily listings with cost-efficient LLM scoring.

Lessons from running an LLM scoring pipeline in production: cost management, function calling architecture, and error handling.

How naive LLM integration drains your SaaS budget and how to fix it with caching, batching, and model routing.

Three production-tested patterns for AI agent memory: structured logs, vector stores, and hybrid checkpointing.

Practical techniques for evaluating LLM outputs in production, from a full-stack AI engineer who ships AI features daily.

A senior AI engineer breaks down the hidden costs of shipping LLM features: edge cases, cost control, error handling, and evaluation.

A senior AI engineer explains why agent memory fails at scale and how to fix it with proven production patterns.

What I learned processing a million job listings with LLMs — architecture, cost control, and when AI beats traditional ML.

A real engineering debrief on how one bad retry pattern and missing token guardrails caused an AI API cost explosion and how to fix it.

A senior AI engineer explains why your LLM pipeline needs cost controls, retry logic, and guardrails before you ship.

Real patterns for graceful degradation, cost-aware routing, and observability in multi-model AI systems.

A senior engineer's guide to building reliable AI agent systems with retry logic, fallback chains, and cost monitoring from real production experience.

A production safety framework for AI agents: guardrails, human-in-the-loop, idempotent actions, and rollback strategies from real deployments.

Production-hardened lessons on LLM guardrails from a developer who's dealt with hallucinations, prompt injection, and runaway costs.

Production-tested patterns for building safe, reliable AI agents that won't destroy your data. Real lessons from the trenches.

A senior engineer's deep dive into building reliable LLM pipelines at scale — architecture, cost management, and failure modes.

Real lessons building a production AI job board pipeline with GPT-4 function calling: batch processing, retries, cost control.