DEEP DIVE

Hands-On AI Agents:

12X BEST tips with LangGraph, LangChain, CrewAI, OpenAI Swarm & Hugging Face LLM

Presented by Dr. Maryam Miradi

Online | Pre-recorded | 27 Minutes

Hosted by:

Dr. Maryam Miradi

About Dr. Maryam Miradi

Maryam Miradi is the CEO and Chief AI Scientist of Profound Analytics.

She has over 20 years of experience in AI model development, holds a PhD in AI, has published 24 articles, authored 2 books, received 5 awards, and has experience coaching over 200 data scientists across 12 industries.

She has been awarded as the Best European Researcher in Future Vision.

RAG & DPR

Incorporate RAG pipelines to fetch relevant, up-to-date documents. Tools like FAISS and ChromaDB can enhance the retrieval process for context-aware generation.
Implement DPR for fast, precise document and passage retrieval, especially for large datasets.

Audio, Image, Tabular Data

Add human-like voices using tools like ElevenLabs or Hugging Face’s TTS models to enhance interactivity.
Use real-world datasets (e.g., Kaggle medical records) or pre-trained models for tasks like pneumonia detection with Hugging Face’s medical imaging tools.

Flow Engineering & Optimizing Performance

Use platforms like Groq for faster token generation or Ollama for efficient local processing with hardware optimization.
Visualize processes as sequential flows, leveraging tools like CrewAI’s pipeline visualization to ensure clarity and logical progression.

🔑 Key Takeaways:

✸ Simplify Prompts: Avoid overly complex instructions; use clear, goal-oriented prompts.
✸ Incorporate Real-World Data: Leverage structured data and pre-trained models for reliability.
✸ Test Components Individually: Validate each agent and tool separately before integration.
✸ Use Retrieval-Augmented Generation (RAG): Enhance outputs with up-to-date, relevant information.
✸ Manage Context Windows: Divide large datasets into manageable chunks to avoid token limitations.
✸ Optimize Workflow with Flow Engineering: Visualize and build workflows step-by-step for clarity.
✸ Enhance Speed and Performance: Use platforms like Groq and Ollama for faster, optimized responses.
✸ Configure Vector Databases Effectively: Tune embedding quality, chunk size, and overlap for accurate retrieval.
✸ Integrate Speech and Audio: Add human-like voices for engaging, dynamic agents.
✸ Use Prompt Templates: Employ dynamic variables to create reusable and flexible agent designs.
✸ Choose Specialized LLMs: Match models to tasks for optimal performance (e.g., reasoning, creativity, image-to-text).
✸ Leverage Advanced Retrieval Methods: Combine RAG with dense passage retrieval (DPR) for precision and efficiency.

🌟 Ready to apply these principles?