LLMs in Production
Cost, latency, evaluation, security, and ops for real LLM applications.
Getting an LLM demo working takes an afternoon. Keeping one fast, affordable, measurable, and safe in front of real users is the actual job. This guide collects every production-focused article on this site into one path: cutting cost and latency, building evaluation pipelines you can trust, defending against prompt injection, and the MLOps needed to keep it all running.
Cost & speed
The two complaints every LLM app gets first.
01A Beginner's Guide to Cost Optimization in LLM Applications
LLM API costs compound fast at scale. Token budgeting, model routing, prompt caching, and batching are the four levers that cut costs without degrading output quality in product...
02Why Your LLM Application Feels Slow
LLM latency usually isn't the model's fault. Synchronous retrieval, sequential tool calls, missing streaming, and cold-start overhead are the architectural decisions that actual...
03LLM Inference Optimization: Quantization, KV Cache, and Serving at Scale
Serving a 70B model cheaply requires quantization, KV cache tuning, continuous batching, and the right serving stack. A systems-level breakdown of vLLM, TGI, speculative decodin...
04Semantic Caching for LLMs: Cut Your API Bill by 60%
Exact-match caching fails for LLMs because users never ask the same question twice in identical words. Semantic caching solves this by comparing query embeddings instead. A cach...
Evaluation
If you can't measure it, you can't ship it twice.
05LLM Evaluation Pipelines: How to Test What Your Model Actually Does
Standard unit tests cannot evaluate LLM outputs because the same question can have dozens of correct answers. This post covers LLM-as-judge scoring, RAGAS metrics for RAG pipeli...
06LLM as Judge: How to Evaluate AI Models Automatically at Scale
Human evaluation of LLM outputs is slow and expensive. LLM-as-judge uses a capable model to score and critique another model's outputs automatically. Here is how it works, where...
07Metrics Beyond Accuracy: Measuring What Actually Matters
Accuracy hides class imbalance. Precision, recall, F1, AUC, and MCC each expose different failure modes. Choose the wrong metric and you'll ship a model that looks good on paper...
Safety & security
Your users are creative. Some are adversarial.
08Prompt Injection Attacks: How LLMs Get Exploited and How to Defend Your Application
Prompt injection turns user input into an instruction override. Indirect injection, jailbreaks, and data exfiltration vectors are all in scope, and defense requires more than an...
09A Beginner's Guide to Building AI Safety Filters
Input classifiers, output filters, and safe-completion layers don't stop all attacks, but they raise the cost of abuse significantly. How to layer them correctly in a production...
Operations
Observability, drift, CI/CD — the unglamorous parts that keep it alive.
10LLM Observability: Tracing, Logging, and Debugging AI Applications
You can't debug what you can't trace. Setting up prompt logging, span tracing, cost tracking, and latency monitoring for production LLM apps, with practical examples using LangS...
11How to Monitor ML Drift in Real Deployments
Data drift, concept drift, and label drift degrade model performance in different ways. How to detect each in production, which statistical tests to use, and when the right call...
12A Beginner's Guide to CI/CD for ML Models (GitHub Actions + Docker + Kubernetes)
ML models need pipelines that test, containerize, and deploy reliably, not just once, but on every retrain. Build a full CI/CD workflow with GitHub Actions, Docker, and Kubernet...
13Why AI Models Fail in the Real World
A model that hits 94% accuracy in the notebook can fail silently in production. The distribution shifts, labeling errors, and deployment gaps that cause most real-world ML failu...
Tools & resources
Free utilities that pair with this guide.
Keep going
Guides that pick up where this one ends.