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Generation Flow Architecture

Generation Flow (genflow) represents the fundamental architectural pattern in Teammately's AI engineering framework. It utilizes a component-based approach to large language model (LLM) orchestration, enabling modular construction of complex AI systems with enhanced maintainability and iterative refinement capabilities.

Architectural Components​

A Generation Flow includes the following principal components:

  • Input Schema - Defines the structured format of variables transmitted through the API from client applications and sets up validation and constraint enforcement.

  • Prompt Book - Contains an AI Agent prompt for specific use case. Can be written manually or generated using Teammately Agent. Also, specifies model selection, para,eters, configuration and supports multi-step generation through sequential prompt orchestration.

  • Knowledge Book - Includes documents text knowledge for retrieval-augmented generation (RAG) and configurable chunking strategies. Specifies embedding model selection for vector representation.

  • Output Schema - Specifies the structured format of variables returned to client applications.

Variables and References​

Within Generation Flow, variables and flow references provide powerful capabilities:

  • Variable Usage - Variables can be accessed in prompts using double curly braces syntax {{variable_name}}. This allows dynamic insertion of input values into prompts.

  • Genflow References - One genflow can reference another as input by using the same syntax: {{genflow_id}}. This enables composition of multiple flows and creation of complex chains.

  • JSON Output - When using JSON schema for output, the prompt must include the word "json" anywhere in the text to properly trigger structured output parsing.

Production Deployment​

When deployed to production environments, Generation Flows are exposed as API endpoints with consistent interfaces. Each deployed flow:

  1. Accepts structured input conforming to the defined Input Schema
  2. Processes the input through the configured Prompt and Knowledge Books
  3. Returns responses structured according to the Output Schema
  4. Provides optional logging and callback capabilities for asynchronous operation

Development Workflow​

The Generation Flow development lifecycle typically progresses through the following phases:

  1. PRD Definition - Establishing requirements and evaluation criteria
  2. Component Development - Creating and configuring individual components
  3. Quick Testing - Preliminary validation against representative cases
  4. Comprehensive Evaluation - Thorough testing across diverse scenarios
  5. Optimization - Refinement based on evaluation insights
  6. Deployment - Production release with monitoring capabilities

This structured approach ensures methodical development of high-quality AI systems aligned with specified requirements.