Prompt engineering and LLM fine-tuning for summarization and analysis

1. Foundational Prompting: Master zero-shot, few-shot, and chain-of-thought (CoT) prompting with models like GPT-3.5/4, focusing on instruction clarity and output formatting. 2. Data Curation: Learn to collect, clean, and structure high-quality datasets for summarization and analysis tasks, understanding the critical link between data quality and model performance. 3. Core Metrics: Understand and apply standard evaluation metrics for summarization (e.g., ROUGE, BERTScore) and analysis (e.g., precision, recall for extraction tasks).

1. Prompt Engineering Patterns: Implement advanced techniques like ReAct (Reasoning + Acting), self-consistency, and meta-prompting for complex, multi-step analysis. 2. Fine-Tuning Workflow: Execute a full supervised fine-tuning (SFT) loop on a base model (e.g., Llama 2, Mistral) using a curated dataset for a specific domain (e.g., legal, financial). 3. Avoid Common Pitfalls: Mitigate prompt injection risks, combat model hallucination through grounding techniques, and manage context window limitations effectively.

1. Architectural Decisions: Evaluate trade-offs between prompt-only solutions (RAG), full fine-tuning, and parameter-efficient fine-tuning (PEFT) methods like LoRA/QLoRA for scalability and cost. 2. System Integration: Design and implement an end-to-end pipeline where a fine-tuned model integrates with a vector database for retrieval-augmented generation (RAG), handling live data streams. 3. Strategic Alignment & Mentoring: Define the ROI of fine-tuning projects, establish evaluation frameworks tied to business KPIs, and mentor junior engineers on responsible AI principles and debugging complex failure modes.