Generative AI Application Development

Foundations of Large Language Models and Generative Architectures

Core Model Architectures

• Mastery of the Transformer architecture, focusing on the self-attention mechanism, multi-head attention, and positional encoding that allows models to process sequential data in parallel.
• Understanding the difference between Encoder-only models (like BERT), Decoder-only models (like GPT series), and Encoder-Decoder models (like T5) and how to select the right architecture for specific application needs.
• Deep dive into tokenization strategies, including Byte-Pair Encoding (BPE) and WordPiece, and how these tokenization methods directly impact model performance and vocabulary efficiency.

Advanced Prompt Engineering and Steering

• Designing complex prompt structures including Few-Shot Prompting, Chain-of-Thought (CoT), and ReAct patterns to improve reasoning capabilities in LLMs.
• Implementing system-level instructions to control output format, tone, and constraints, ensuring model responses are predictable and structured for programmatic consumption (e.g., JSON outputs).
• Strategies for mitigating model hallucination through factual grounding techniques and prompt-based verification loops.

Vector Databases and Retrieval Augmented Generation (RAG)

Semantic Search and Vector Embeddings

• Generating high-dimensional embeddings using pre-trained models to convert text, images, or code into numerical vectors that represent semantic meaning.
• Calculating vector similarity using distance metrics such as Cosine Similarity, Euclidean distance, and Dot Product to identify the most relevant data for a given user query.
• Optimizing vector database performance through indexing algorithms like HNSW (Hierarchical Navigable Small World) and IVF (Inverted File Index) for sub-millisecond retrieval speeds.

Advanced RAG Implementations

• Architecting end-to-end RAG pipelines that fetch private, dynamic, or non-public data to augment model knowledge in real-time.
• Implementing document chunking strategies, including recursive character splitting and semantic chunking, to ensure optimal context retrieval.
• Developing hybrid search systems that combine traditional keyword-based search (BM25) with vector-based semantic search for superior result accuracy.

Application Development and Orchestration

Orchestration Frameworks

• Building complex chains and DAGs (Directed Acyclic Graphs) to link multiple LLM calls, tool executions, and data processing steps.
• Managing state across multi-turn conversations using memory buffers, windowing, and entity-based memory storage to keep interactions contextually aware.
• Integrating external tools and APIs as functions, allowing the model to interact with live databases, search engines, and calculation tools for dynamic task execution.

Agentic Systems and Multi-Agent Workflows

• Designing autonomous agents that can evaluate their own progress, self-correct based on feedback, and decide which tools to use for specific sub-tasks.
• Creating collaborative multi-agent architectures where specialized agents communicate to solve high-complexity problems beyond the capability of a single model instance.
• Implementing human-in-the-loop (HITL) workflows to verify agent output at critical decision points before final action execution.

Model Evaluation, Security, and Optimization

Evaluation Metrics and Benchmarking

• Deploying automated evaluation frameworks to measure model performance on specific tasks using metrics like ROUGE, BLEU, and LLM-as-a-judge approaches.
• Establishing a robust testing loop to evaluate retrieval quality using precision, recall, and Mean Reciprocal Rank (MRR) for document retrieval systems.

Model Security and Optimization

• Implementing prompt injection defenses through rigorous input sanitization, structural constraints, and output validation layers.
• Techniques for model fine-tuning including Parameter-Efficient Fine-Tuning (PEFT) and Low-Rank Adaptation (LoRA) to adapt models to domain-specific datasets without the need for massive compute resources.
• Optimizing latency and cost via model quantization, caching of frequently used responses (Semantic Caching), and effective selection of model sizes based on task complexity.