📰 Dev.to · Abdul Rehman

A senior engineer's walkthrough of building a production LLM scoring pipeline: architecture, cost control, function calling, and real trade-offs.

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.

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.

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.

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.

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

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.