Here you can find some of my presentations:

  • LLM Fundamentals & Limitations An overview of large language models, including generative language modeling theory, training steps (Pre-training, SFT, Alignment), perplexity (PPL) metrics, and core constraints.

  • Vector Search & Embeddings 101 An introduction to semantic and vector search, inverted indexing (TF-IDF/BM25), embeddings representational learning (Word2Vec, BERT), and vector databases. Includes an interactive similarity graph embed.

  • RAG & Advanced Retrieval Techniques An introduction to advanced RAG concepts, hybrid search (Sparse + Dense), Reciprocal Rank Fusion (RRF), Bi-Encoders vs. Cross-Encoders, chunking strategies, and production RAG architectures.

  • Building Agentic Systems An overview of autonomous agents, including planning loops (ReAct, Tree of Thoughts), memory architectures (short-term vs. long-term memory), tool calling, and multi-agent systems (MetaGPT SOPs).

  • Evaluating Ranking Systems An overview of ranking metrics, including MRR, NDCG, F1 Score, and Size Accuracy.

  • Career Timeline (PDF) A visual walkthrough of my career journey, milestones, and technical path over the last 15 years in software engineering, cloud architecture, data science, and AI.