llm
60 notes tagged llm in the Applied LLMs topic. Putting language models to work: retrieval, prompting, evaluation and the behavior you only learn in production.
- Context7 breakdown
Technical analysis of Context7, an intelligent documentation indexing and retrieval system that transforms raw library docs into AI-optimized, ranked snippets for…
- Dify breakdown
Technical analysis of the Dify platform, its architecture, and engineering decisions that enable scalable LLM application development.
- Mem0 & Mem0-Graph breakdown
Technical analysis of Mem0, a scalable memory architecture for LLMs, and its graph-based variant, Mem0-Graph, designed for long-term conversational coherence.
- Ax framework breakdown
Technical analysis of the Ax TypeScript framework for building LLM-powered agents with DSPy capabilities.
- Crawl4AI breakdown
Deep dive into Crawl4AI's architecture, data structures, and algorithms - from async pipelines and strategy patterns to browser management and intelligent content…
- Zen MCP breakdown
Technical analysis of the Zen MCP (Model Context Protocol) Server architecture, implementation, and design patterns.
- AI agent explained: LLMs, workflows and agents
Most people confuse LLMs, workflows, and agents, but understanding these three levels unlocks the real potential of AI automation. The key difference lies in who makes…
- Append-only concept embedding log
RFC proposing a novel approach to track concept evolution using TimescaleDB with pgvector/pgvectorscale, enabling historical semantic analysis and preventing…
- Build a brain that never forgets
Explore how we solved the catastrophic forgetting problem in LLMs by building an external knowledge system that mimics learning through context manipulation, without…
- Data flow in Brainery
How data flows through Brainery's second brain system, from Discord, Memo Blog, and GitHub sources to TimescaleDB via MCP, with an LLM-powered interface for natural…
- Database design and philosophy
A migration-less, append-only database design using TimescaleDB's hypertables and continuous aggregates to capture observational data and detect emerging patterns…
- Building use-cases
Building systems that actually learn and get smarter over time is hard, facing challenges with traditional databases and LLMs. This post outlines an approach to building…
- Promoting raw data to insight
This post explains the multi-stage process of transforming raw data into structured information, and then synthesizing that information into persistent insight and…
- Create slides with Overleaf and ChatGPT
This article shares a workflow for making slide decks with Overleaf and ChatGPT. It solves issues like slow content creation using ChatGPT and formats with Overleaf’s…
- Let AI ask you
Most people give AI commands and expect perfect results, but the most effective approach flips this dynamic. Letting AI ask you questions transforms shallow outputs into…
- Mental models for prompting
Essential mental models to improve your LLM interactions and prompt engineering effectiveness
- Quantization for large language models
As large language models (LLMs) continue to evolve, their parameter counts grow exponentially, with some models reaching trillions of parameters. This exponential growth…
- Natural language to database queries: Text-to-MongoDB
An exploration of natural language to database query systems using MongoDB, examining system prompts and implications for data engineering and agentic workflows.
- GraphRAG - building a knowledge graph for RAG system
In baseline Retrieval Augmented Generation (RAG), sometimes the result might not be accurate as expected since the query itself have multiple layers of reasoning or the…
- Guardrails in llm
Inspite of having strength to process and produce highly coherent human-like, behavior of LLM is unpredictable, so the need of a safety mechanisms and boundaries that…
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- ReAct(reason + act) in LLM
Working with Large Language Models (LLMs) may seem straightforward at first glance. Initially, we might think that simply prompting the AI model with a task is…
- ReWOO: Reasoning without observation - a deeper look
In the process of improving Large Language Model (LLM) performance, many techniques have been proposed. The Augmented Language Model (ALM) approach boosted LLM accuracy…
- Model selection
Learn how to choose the right AI model for your needs. Explore key factors like accuracy, privacy, and cost. Compare commercial vs open-source options and API vs…
- Logging
Logs are like the footprints of your LLM, tracking every move it makes. We will look at how logging can help you see beneath the top layer of a system, which can help…
- Metrics
Metrics give you the rundown on how your LLM’s performing. We will show how to use these metrics to identify issues, increase efficiency, and make changes for improved…
- Tracing
Tracing is like following your LLM’s journey, step by step. We will explain how tracing makes it easy to identify and address problems by allowing you to track the…
- Intent classification by LLM
User intent classification is a crucial aspect of conversational AI, start with machine learning models, but now advanced language models (LLMs) are being explored for…
- LLM as a judge
With the robust growth of LLM models currently, there is a new method is used to evaluate the performance of large language models (LLMs): LLM-as-a-Judge, also known as…
- Use cases for LLM applications
Explore the diverse applications of large language models (LLMs) and AI in both enterprise and consumer sectors. Learn about key use cases across data analysis, content…
- The rise of AI applications with LLM
Discover how the rapid surge in artificial intelligence, led by models like ChatGPT, Claude, and Gemini, is reshaping industries and democratizing AI development. This…
- Evaluation guidelines for LLM applications, part 1: the evaluation checklist
A product evaluation checklist and the business and user expectations to settle before integrating a third-party LLM into an application.
- Evaluation guidelines for LLM applications, part 2: types of evaluation and considerations
The difference between model evaluations and product evaluations, plus coverage, monitoring, user feedback and when fine-tuning is needed.
- Prevent prompt injection
Nowadays, Large Language Models (LLMs) have become integral to various applications. However, with great power comes great responsibility, and the rise of LLMs has…
- Evaluate chatbot agent by user simulation
When building a chatbot agent, it's important to evaluate its performance and user satisfaction. One effective method is user simulation, which involves creating virtual…
- Journey of thought prompting: Harnessing AI to craft better prompts
Journey of Thought Prompting is an innovative technique that leverages AI to craft more effective prompts for large language models. This approach uses the analytical…
- Multi-agent collaboration for task completion
In AI integrated systems, instead of put all workload on a single agent, we can apply divide and conquer strategy to distribute workload to multiple agents. This…
- Evaluating caching in RAG systems
Caching is a vital technique that boosts performance by storing frequently accessed information. Let's see how it works with RAG. In this article, we will know how cache…
- Evaluating search engine in RAG systems
Hybrid search comes to solving problems in vector search and full-text search. Let's see how it works. In this article, we will know how hybrid search approach and how…
- What is Generative UI?
An introduction to Generative UI (genUI), a user interface that generates interactive elements in response to user needs using AI, enhancing UX in chat applications. The…
- Function calling in AI agents
Function calling is a critical component in the architecture of AI agents, facilitating the integration of external functionalities and resources. This note explores how…
- Streamlining internal tool development with managed LLMOps: A Dify case study
Discover how managed LLMOps platforms like Dify streamline the development of AI-powered internal tools. Learn about the challenges of integrating LLMs, the benefits of…
- Thumbs up and thumbs down pattern
The article talks about how important human feedback is for making large language models better, especially using thumbs up and down. It explains the Pearson correlation…
- Building agent supervisors to generate insights
In the rapidly evolving field of artificial intelligence, the concept of agent supervisors has emerged as a powerful approach to orchestrating multiple AI agents for…
- Design feedback mechanism for LLM applications
Improving AI models requires collecting accurate user feedback,which can be difficult. The article explores more into the importance of collecting human feedback and…
- Proximal policy optimization
Proximal policy optimization (PPO) is an algorithm that aims to improve the stability of training by avoiding overly large policy updates. It is a popular and effective…
- Multimodal: in rag
In spite of having taken the world by storm, Large Language Models(LLM) still has some limitations such as limited context window and a knowledge cutoff date.…
- Explaining gradient descent in machine learning with a simple analogy
Gradient descent is a fundamental optimization algorithm in machine learning. It's a way for models to learn from data and improve their accuracy by gradually adjusting…
- A grand unified theory of the AI hype cycle
An exploration of the cyclical nature of AI development, tracing the rise and fall of new technologies within the field, and how this pattern has repeated throughout…
- History of structured outputs for LLMs
When Large Language Models (LLMs) becomes popular and an essential tool for growing businesses, it signals a transition toward more complex and efficient data…
- Developing rapidly with generative AI
Generative AI overview and the different stages of building an LLM-powered feature
- A story map for building with LLMs
A comprehensive guide outlining the journey for engineers developing applications on top of Large Language Models (LLMs), covering key stages from understanding AI…
- Adversarial prompting in prompt engineering
An overview of adversarial prompting in prompt engineering, focusing on understanding risks associated with Large Language Models (LLMs) and strategies for mitigating…
- LLM's accuracy - self refinement
An overview of self-refinement, a technique where Large Language Models (LLMs) evaluate and improve their own output without the need for supervised data or…
- Reward model
A Reward model is a critical component in Reinforcement Learning for Large Language Models (LLMs), designed to evaluate and score the quality of generated responses. It…
- Q learning
An introduction to Q-learning, a model-free reinforcement learning algorithm used to learn optimal policies in Markov Decision Processes.
- Building blocks of a RAG pipeline
How chunking, hierarchical tree summaries, re-ranking, query caching and vector-store chat memory fit together around a vector database in an LLM application.
- Introduction to reinforcement learning and its application with LLMs
An introduction to Reinforcement Learning (RL), a machine learning method where an agent learns to make decisions by interacting with an environment. This article covers…
- How we created an AI powered interview system using OpenAI's ChatGPT
Discover InterviewGPT, an AI-powered ChatGPT system that automates technical interviews by generating tailored questions and evaluating answers for engineers at all…
- Choosing a vector database for LLM applications
An overview of vector databases and their application in LLM systems. This article covers the history and core concepts of vector databases, their use cases,…
- Foundation models: the latest advancement in AI
An overview of foundation models, their versatility in generative AI tasks, and their impact across various domains like NLP, image generation, and scientific research.…