Article archive
- How to create LLM tools from any Python SDK using langchain-autotools
- How to crawl websites for LLMs - using Firecrawl
- Azure AI Studio: How to evaluate and upgrade your models, using the Prompt Flow SDK
- Saving costs with LLM Routing: The art of using the right model for the right task
- RAG, Embeddings and Vector Search with Google BigQuery
- Parsing pdf, word and excel documents with GPT-4o
- How to classify, describe and analyze images using GPT-4o vision
- What is Open WebUI? The best self hosted, open source ChatGPT alternative?
- What is the VertexAI model garden - a practical example: Deploying Llama 3.1 and Claude 3.5 Sonnet
- Fine-tune OpenAI's GPT-4o mini LLM AI model for free
- Neo4j LLM Knowledge Graph Builder: How to Create Knowledge Graphs for RAG
- How to Chat with Your BigQuery? Introducing semantic search to databases.
- How to finetune LLMs
- LLM Document Extraction: How to use AI to get structured data from legacy documents
- Using AI directly from your database - with PostgreSQL and pgai
- KAN: Is this the end of feedforward networks?
- LLM Safety with Llama Guard 2
- How to use AI to create a PowerPoint presentation?
- What is the Google Vertex AI Agent Builder - A practical example: Google Search with Slack.
- BitNet: LLM Quantization at its Extreme
- Chat with your Confluence: A Step-by-Step Guide using Airbyte, Langchain and PGVector
- Advanced RAG: Improving Retrieval-Augmented Generation with Hypothetical Document Embeddings (HyDE)
- GenAI: Technological Masterpiece or Ecological Disaster?
- 11 Proven Strategies to Reduce Large Language Model (LLM) Costs
- How to boost your database performance with OpenAIs new v3 embeddings
- Introduction to Retrieval Augmented Generators (RAG): Enhancing Virtual Assistants with Domain-Specific Knowledge
- Advanced RAG: Increase RAG Quality with ColBERT Reranker and llamaindex
- Advanced RAG: Recursive Retrieval with llamaindex
- How to Set Up a Secure, Self-Hosted Large Language Model with vLLM & Caddy
- Improving Retrieval Augmented Generation: A Step-by-Step Evaluation of RAG Pipelines
- Integrating enterprise knowledge with LLMs