Prompt Engineering Patterns That Actually Work in Production
The gap between a working demo and a reliable production system is engineering discipline around inputs, outputs, and failure handling.
Check Out Our latest Insights and Playbooks
Small Language Models Are the Future of Agentic AI
The future of AI lies in compact, efficient small language models that deliver powerful capabilities directly on edge devices.
Context Window Management: Why Bigger Is Not Always Better
A 128K context window does not mean you should use 128K tokens. The evidence shows that more context often means worse answers, higher costs, and slower responses.
Evaluating LLM Providers: A Procurement Framework
Choosing an LLM provider is not a model quality decision. It is a vendor risk, data governance, and total cost of ownership decision.
Fine-Tuning vs. RAG: When Each Strategy Wins
Fine-tuning and RAG solve different problems. Choosing wrong wastes months of engineering effort. Here is how to decide.
Embedding Model Selection for Production RAG
The embedding model is the foundation of your RAG system. Choose wrong and no amount of prompt engineering or re-ranking will compensate.
The Managed-to-Open-Weight Migration: A Framework for LLM Cost Control
As production volume scales, the shift from managed APIs to hosted open-weight models isn't just about cost — it's about latency, privacy, and long-term IP ownership.
Playbook: Testing Non-Deterministic LLM Outputs in CI
Vibe checks do not scale. This playbook covers deterministic evaluators, model-graded rubrics, and assertion-based testing patterns that bring LLM outputs under CI discipline.
Building a RAG Evaluation Framework From Scratch
Deploying a RAG system is straightforward. Knowing whether it actually works is harder.
Research
LLM Latency Budgets: What Production Teams Actually Measure
Users do not experience benchmark throughput. They experience time-to-first-token, streaming speed, and total wait time. Here is how production teams measure and budget for each.
The Real Cost of LLM Hallucination in Production
Hallucination is not a research curiosity. It is an operational cost with legal, reputational, and engineering consequences that compound at scale.
Beyond the Prompt: Why Data Engineering is the New Prompt Engineering
The most performant LLM systems are moving logic out of the prompt and into the data layer. Prompt engineering gets the attention. Data engineering gets the results.
Building a Locally Deployed, High-Performance Multi-Layer RAG System
A lean, locally deployable RAG framework that allows a compact 1.5B model to rival much larger models on domain tasks.
