# Developer Guide

This manual is for engineers building, extending, or integrating the AI-enabled Clinical Decision Support System. Use it when onboarding to the codebase, implementing a new module, or understanding the boundaries between the intake, alerting, and doctor query subsystems.

## Overview

This section describes the system's architecture: the AI-guided intake engine, the senior consultation alert pipeline, the doctor intelligence query interface, and the session resumption mechanism.

## Prerequisites

This section covers what developers need before contributing: local environment setup, understanding of the role-based access model, and familiarity with the Daksh implementation pipeline for this project.

## Core procedures

This section holds the numbered workflows developers follow most often: running the development environment, adding a new questioning protocol, writing and activating an alert pattern, and deploying a module update.

## Reference

This section documents the data model, API surface between modules, role-based access boundaries, and the audit log schema.

## Troubleshooting

This section covers common development issues: local environment failures, AI model integration errors, and session state inconsistencies during testing.

## Changelog

This section records API changes, architectural decisions, and breaking changes relevant to engineers working on the system.