Requirement Engineering
SWE.1 – Software Requirements Analysis
Welcome in 2021,I know its already been some months but I hope you had a good and motivated start into the new year. What could be better than starting by reading the newest blog post about “…
Multi-agent AI system in Google Cloud | Cloud Architecture Center
Design robust multi-agent AI systems in Google Cloud.
Design Pattern
A Guide on 12 Tuning Strategies for Production-Ready RAG Applications
Strategies and parameters you can tune to improve the performance of Retrieval-Augmented Generation (RAG) applications for production.
Design Pattern
Building LangGraph: Designing an Agent Runtime from first principles
In this blog piece, you’ll learn why and how we built LangGraph for production agents—focusing on control, durability, and the core features needed to scale.
Deep Dive
Tutorial
Context Engineering in Manus
Manus approaches to context engineering.
Design PatternAI Enhanced Requirements Traceability Using MBSE LLM Complex Systems
Challenges in applying large language models to requirements engineering tasks
Generating Requirements for ADAS Cameras Using an LLM-Based Approach
PapersUnleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration
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Context Engineering in Multi-Agent Systems
The blog explores how to apply practical context engineering techniques using Agno to build AI agents that are faster, more efficient, and better at collaboration. It covers core techniques that include crafting precise system messages, selectively managing context to reduce token use, applying few-shot learning to teach behavior, and coordinating multi-agent teams effectively.
LLMs Reproduce Human Purchase Intent via Semantic Similarity Elicitation of Likert Ratings
Report
Paper
Databricks Fine-Tuning: MLflow Sweep Comparison & Fast Model Serving Demo (Llama/ Unsloth)
Join Ryan Cicak, Solutions Engineer at Databricks, as he explores the art of fine-tuning models using serverless GPU compute. Discover how to pull models from Hugging Face, fine-tune them with ease, and serve them via API.
Databricks Certified Generative AI Engineer Associate
Evaluation-Driven Development of LLM Agents: A Process Model and Reference Architecture
Evaluation-Driven Development of LLM Agents
Unlike deterministic systems, an LLM agent’s output is often probabilistic, meaning multiple responses may be valid within a given scenario.
PaperEval Driven System Design - From Prototype to Production
This cookbook provides a practical, end-to-end guide on how to effectively use evals as the core process in creating a production-grade a...
Design Pattern
An LLM-as-Judge Won't Save The Product—Fixing Your Process Will
Applying the scientific method, building via eval-driven development, and monitoring AI output.
Building product evals is simply the scientific method in disguise. That’s the secret sauce. It’s a cycle of inquiry, experimentation, and analysis.
Concept
Building resilient prompts using an evaluation flywheel
This cookbook provides a practical guide on how to use the OpenAI Platform to easily build resilience into your prompts. A resilient prom...
Strategy
Effective context engineering for AI agents
Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems.
Concept
How Long Contexts Fail
Taking care of your context is the key to building successful agents. Just because there’s a 1 million token context window doesn’t mean you should fill it.
Deep Dive
Context Rot: How Increasing Input Tokens Impacts LLM Performance | Chroma Research
What are Large Language Models? | NVIDIA Glossary
Large language models (LLMs) are deep learning algorithms that can recognize, summarize, translate, predict, and generate content using very large datasets. Explore all about LLMs solutions.
Resources
Mastering LLM Techniques: Inference Optimization | NVIDIA Technical Blog
Stacking transformer layers to create large models results in better accuracies, few-shot learning capabilities, and even near-human emergent abilities on a wide range of language tasks.