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Constructing a dynamic, role-aware group chat system demands careful prompt architecture—especially when targeting models with limited instruction-following fidelity, such as gpt-3.5-turbo. While newer open-weight models like deepseek-chat offer stronger consistency at lower cost, the core challenge remains: how to reliably bind responses to di ...

This article explores the prompt template structure for ChatGLM3 within the LangChain framework, focusing on how the model handles tool execution and knowledge-based responses. Prompt Template Definition The following configuration defines the instruction set for the ChatGLM3 agent when processing user requests: PROMPT_TEMPLATES["agent_cha ...

Generative AI systems are increasingly deployed across industries and research institutions. Developers and users interact with these systems through prompts or prompt engineering. Despite being widely discussed, prompt terminology remains inconsistent due to the field's nascent nature. This analysis presents a structured taxonomy of prompt tec ...

Modeling prompts as executable code structures enables precise, modular, and reusable interactions with large language models (LLMs). This approach treats the LLM as a software package with well-defined interfaces and capabilities. Core Capabilities input(): Accept user input output(): Return processed results Functional Modules Text Processi ...

Tool Scheema and Invocation Definition The specialized task routing mechanism is implemented as a dedicated function-calling tool. The underlying JSON schema defines a strict contract for dispatching domain-specific operations, requiring a target identifier and accepting an optional parameter string. { "tool_name": "SkillDispat ...