Just completed my #LLMZoomcamp final project: AA Assistant a RAG-powered chatbot providing trustworthy Alcoholics Anonymous information to people seeking help

Tech Stack
• NVIDIA NIM for LLM inference
• Jina embeddings v2 for Spanish/English
• FastAPI + Qdrant vector DB

github.com/marcelonieva...

Completed Module 3 homework of #llmzoomcamp! ✅
📊 Measured hit-rate & MRR with minsearch & Qdrant
🧮 Built TF-IDF→SVD embeddings for vectors
🔍 Tested Q&A vs Q-only search impact
📏 Compared LLM vs FAQ via cosine & ROUGE

Completed Module 3 of #llmzoomcamp! ✅
🎯 Built ground-truth with LLM help
📊 Ranked text & vector search results
🧮 Ran cosine-sim & LLM-as-judge evals
🔍 Compared offline vs online RAG metrics

Completed the Module Agents homework of #llmzoomcamp! ✅
🔹 Defined a function to generate fake weather data and created its tool description
🔹 Added a tool to set weather data and documented it
🔹 Set up an MCP server and implemented a client to interact with it

🎉 Completed Workshop: From RAG to Agents of #llmzoomcamp! ✅
🔹 Built an agentic RAG application
🔹 Learned about agentic search and decision-making
🔹 Implemented function calling for smarter interactions
🔹 Explored PydanticAI for easier agent deve

Completed the Module Homework of #llmzoomcamp! ✅
🔹 Installed and used dlt with Qdrant support
🔹 Loaded FAQ data into a Qdrant vector database
🔹 Created and ran a dlt pipeline for data ingestion
🔹 Explored embedding models used during data insertion
Hands-on, practical learning! 📊 #llmzoomcamp

Completed the DLT Workshop of #llmzoomcamp! ✅
🔹 Explored how dlt simplifies modern ELT pipelines
🔹 Loaded and transformed data from APIs and files
🔹 Built a knowledge graph with Cognee
🔹 Queried data using natural language
🔹 Connected RAG systems with structured memory
Hands-on and eye-opening! 💡🧠

Completed the Module 2 homework of #llmzoomcamp! ✅
🔹 Set up Qdrant with Docker
🔹 Generated embeddings with FastEmbed
🔹 Indexed FAQ data
🔹 Performed semantic search
🔹 Built a RAG pipeline using vector search
Hands-on, powerful stuff! 💪🧠

Just wrapped up Module 2 of the LLM Zoomcamp! 🚀
🔍 Learned about vector search
📦 Embedded & indexed text data
🧠 Used Qdrant as a vector DB
🤖 Combined RAG with semantic search
Getting closer to building smart, context-aware AI systems!
#LLM #RAG #Qdrant #llmzoomcamp

Just finished the homework for Module 1 of the LLM Zoomcamp! 🧠💻
✅ Ran Elasticsearch
✅ Indexed FAQ data
✅ Built custom queries
✅ Filtered results
✅ Created prompts for LLMs
✅ Counted tokens & estimated costs
Hands-on RAG in action!
#LLM #RAG #AI #llmzoomcamp

Completed Module 1 of LLM Zoomcamp: A Free Course on Real‑Life Applications of LLMs! In 10 weeks we’ll build AI systems that answer your knowledge base using RAG, vector search, evaluation & more.
Module 1: LLM & RAG intro, built a RAG Q&A pipeline. #AI #RAG #llmzoomcamp
github.com/DataTalksClu...

Module 4: Trading Strategy and Simulation. Moving beyond prediction into the realm of trading strategy. Learn about trading fees, risk management, and market timing. Simulate financial results and explore long-term investment strategies. #smazoomcamp #machinelearning

Week 3: Explored categorical variables, decision trees, and hyperparameter tuning in finance with Python. Boosting model accuracy step by step! #smazoomcamp

Crushed Module 2 of #smazoomcamp
Leveled up my Python data wrangling skills with Pandas. Now I can handle all sorts of data types and manipulate them like a pro.
Feeling confident creating new features like day of the week and growth rates. Even incorporated technical indicators with TaLib