Context Engineering in Manus
Manus approaches to context engineering.
Design PatternAI Enhanced Requirements Traceability Using MBSE LLM Complex Systems
Requirement EngineeringChallenges 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.
Mastering LLM Techniques: Customization | NVIDIA Technical Blog
Large language models (LLMs) are becoming an integral tool for businesses to improve their operations, customer interactions, and decision-making processes. However, off-the-shelf LLMs often fall…
The Attention Mechanism in Large Language Models
Check out the latest (and most visual) video on this topic! The Celestial Mechanics of Attention Mechanisms: https://www.youtube.com/watch?v=RFdb2rKAqFwAtten...
Introduction - Hugging Face LLM Course
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
How to become a NVIDIA-Certified Associate: Generative AI LLMs (NCA-GENL) | LinkedIn
When Nvidia announced its Generative AI certification tracks at GTC in March 2024 — the LLM-focused NCA-GENL, as well as the multimodal NCA-GENM — it was clear to me that I wanted to give it a shot, for various reasons: My Google Cloud-certified knowledge from 2020 and 2021 felt outdated (see summar
NVIDIA Exam
How Anthropic Built a Multi-Agent Research System
In this article, we will understand the architecture of the multi-agent research system that Anthropic built.
Design Pattern
Context Engineering: Rapid Agent Prototyping - Jason Liu
How to test agent ideas in hours using Claude Code SDK without building orchestration infrastructure - proven methodology from consulting engagements