Reconciling AI Benchmarks and Developer Productivity
Explore the gap between AI benchmark performance and real-world developer productivity in this in-depth analysis.
Cesar Miguelañez
How to Add LLM Testing to GitHub Actions
Automate LLM testing in GitHub Actions: secure API keys, organize datasets, run Latitude evaluations, and fail builds on regressions.
LLM Prompts with External Event Triggers
Choose the right trigger—webhooks, queues, polling, or SSE—to balance latency, scalability, and reliability for production LLM workflows.
Open-Source vs Proprietary LLMs: Ethical Trade-Offs
Choosing between open-source and proprietary LLMs forces a trade-off: transparency and control versus centralized safety and convenience.
Scaling LLMs with Serverless: Cost Management Tips
Practical strategies to cut serverless LLM costs: model right-sizing, prompt and context optimization, caching, batching, routing, and real-time observability.
How Production AI Agents Work: Reliability & Practices
Discover how production AI agents operate, their reliability, and best practices based on recent empirical findings.
Real-Time Observability in LLM Workflows
Real-time observability uses metrics, traces, alerts, and live evaluations to detect latency, cost, and quality issues across LLM workflows.
Best Practices for Domain-Specific Model Fine-Tuning
High-quality data, PEFT methods (LoRA/QLoRA), and expert feedback enable efficient, reliable domain-specific model fine-tuning on limited resources.
How to Identify and Reduce Dataset Bias in LLMs
Only systematic detection, counterfactual augmentation, adversarial debiasing, and continuous monitoring can meaningfully reduce dataset bias in language models.
How to Improve LLM Evaluation with Domain Experts
Discover how involving domain experts improves LLM evaluation, prevents harmful AI errors, and ensures ethical AI deployment.
