Advances in Large AI Models: Prompt Engineering, RAG, Agents, Fine‑Tuning, Vector Databases and Knowledge Graphs

Large AI models are reshaping the artificial‑intelligence landscape, evolving from simple prompt engineering to ambitious goals such as artificial general intelligence (AGI). This article reviews the practical progress of large models and the key technologies that enable them.

Prompt Engineering

Prompt engineering designs specific prompts to guide language models toward desired outputs. A structured prompt typically follows the pattern:

Prompt = 角色 + 任务 + 要求 + 细节【步骤拆解、范例说明，技巧点拨等】

Structured prompts improve model understanding, consistency, efficiency, and reduce ambiguity by providing clear instructions and required details.

Retrieval‑Augmented Generation (RAG) and Knowledge Bases

RAG combines retrieval from external knowledge bases with generation, offering strong explainability and up‑to‑date information. Its workflow consists of three stages: retrieval, utilization, and generation.

Typical RAG architecture can be expressed as:

RAG = LLM+知识库

Knowledge bases are built by vectorizing documents (loading, splitting, embedding) and storing the embeddings in a vector database for efficient similarity search.

AI Agents

Agents are autonomous entities that perceive, plan, act, and remember. Their core formula is:

Agent = LLM+Planning+Tool use+Feedback

Agents follow a PDCA (Plan‑Do‑Check‑Act) cycle to decompose tasks, invoke tools or APIs, evaluate outcomes, and iterate until goals are met.

Vector Databases

Vector databases store high‑dimensional embeddings generated from text, images, audio, etc., enabling fast similarity search for unstructured data. They are essential for the memory component of large models.

Knowledge Graphs

Knowledge graphs represent entities and their relationships in a graph structure, supporting semantic reasoning, fraud detection, and enriching agent context.

Fine‑Tuning

Fine‑tuning adapts pre‑trained large models to specific domains, reducing training cost and improving performance. Two main approaches are full‑parameter fine‑tuning (FFT) and parameter‑efficient fine‑tuning (PEFT), which includes supervised fine‑tuning (SFT), reinforcement learning from human feedback (RLHF), and reinforcement learning from AI feedback (RLAIF).

Function Calling

Function calling lets models output JSON arguments for user‑defined functions, enabling seamless integration with external APIs. Example:

gpt-3.5-turbo-0613

Developers then execute the function (e.g., get_stock_price ) with the provided parameters.

Artificial General Intelligence (AGI)

AGI aims to achieve human‑level or superior intelligence across all tasks. The convergence of prompt engineering, agents, RAG, vector databases, and knowledge graphs forms a collaborative AI ecosystem that brings us closer to this goal.