Fine-tuning
Definition
Fine-tuning is the process of continuing the training of a pre-trained AI model on a smaller, task-specific dataset to improve performance on that task. Fine-tuning trades generality for specialization and is one of several adaptation methods (alongside prompting, RAG, and adapters).
Why it matters
The business case for Fine-tuning.
Fine-tuning is expensive and often unnecessary. Most gains from 'fine-tuning' can be captured with better prompting or retrieval. Knowing when to fine-tune and when not to is a major cost lever.
Related terms in AI Architecture
Cognitive Architecture
A Cognitive Architecture is the structural design of an AI reasoning system, including how it perceives input, accesses memory, plans actions, and learns from feedback. Cognitive architectures are what differentiate sophisticated AI from simple model wrappers.
PRISM
PRISM is SynthesisArc's seven-layer cognitive architecture for enterprise AI. The layers, perception, context, memory, reasoning, planning, action, and learning, combine deterministic and generative AI to deliver consistent, auditable outcomes.
LLM (Large Language Model)
A Large Language Model (LLM) is a foundation model trained on massive text datasets to predict and generate language. GPT, Claude, Gemini, and Llama are all LLMs.
Agentic AI
Agentic AI refers to AI systems that autonomously execute multi-step tasks toward a defined goal, using reasoning, tool use, memory, and self-correction. Agentic AI moves beyond chatbots that respond to systems that act.
Multi-Agent System
A Multi-Agent System is a coordinated set of AI agents working together on a shared goal, sharing context, handing off tasks, and avoiding conflicts. Multi-agent systems are required for any workflow that crosses departmental or functional boundaries.