Request: Produce a Detailed Design Document

## Background

As this project progresses beyond the initial requirements and proof-of-concept phase, we require a comprehensive technical design document that clearly describes the proposed architecture and implementation approach. This document will serve as the primary technical reference for reviewers, mentors, and future contributors throughout the project lifecycle.

## Objective

Produce a detailed design document describing the complete architecture and implementation of the log-analysis project, including the rationale behind key design decisions and how the various components interact.

The document should be sufficiently detailed that another developer could understand, extend, or implement the solution from the design alone.

## Required Content

The design document should include, as a minimum:

### 1. Project Overview

* High-level objectives
* Scope of the project
* Functional and non-functional requirements
* Expected inputs and outputs

### 2. System Architecture

* Overall system architecture diagram
* Description of each major component
* Responsibilities of each component
* Component interactions
* Data flow through the system

### 3. Implementation Design

Describe how each major feature will be implemented, including:

* Log ingestion
* Log parsing
* Event classification
* Event prioritisation
* Time-window filtering
* AI/LLM integration
* Recommendation generation
* Reporting/output generation
* Configuration management

Where appropriate, include UML diagrams, flow diagrams, or sequence diagrams.

### 4. Data Model

Describe:

* Internal data structures
* Log event representation
* Metadata captured
* Intermediate processing formats
* Output schema(s)

### 5. AI/LLM Architecture

Provide details on:

* Which LLM(s) are expected to be used
* Prompt engineering strategy
* Context construction
* Token management
* Handling of large log files
* Mitigation of hallucinations
* Confidence scoring (if applicable)
* Privacy and security considerations

### 6. Processing Pipeline

Describe the complete end-to-end workflow, including:

1. Input acquisition
2. Parsing
3. Event extraction
4. Event categorisation
5. Correlation
6. AI analysis
7. Recommendation generation
8. Report generation

Include workflow diagrams where appropriate.

### 7. Extensibility

Explain how the design supports future enhancements, such as:

* Additional log formats
* Additional AI providers
* New event classifiers
* New recommendation engines
* Plugin or modular architecture

### 8. Error Handling

Describe how the system will handle:

* Invalid log formats
* Corrupted files
* AI failures
* Timeouts
* Partial processing failures
* Recovery strategies

### 9. Dependencies

List:

* External libraries
* AI frameworks
* Runtime requirements
* Development dependencies
* Third-party services

### 12. Risks and Mitigations

Identify technical risks together with proposed mitigation strategies.

## Deliverables

The completed design document should:

* Be committed to the repository (for example under `docs/design.md` or an equivalent location).
* Include clear diagrams where appropriate.
* Be written in sufficient detail to guide implementation.
* Be maintained as the design evolves throughout the project.

## Acceptance Criteria

* [ ] Complete architecture documented.
* [ ] Component interactions clearly described.
* [ ] Processing pipeline documented with diagrams.
* [ ] Data structures and interfaces defined.
* [ ] AI/LLM design documented.
* [ ] Error handling and scalability considerations included.
* [ ] Testing strategy documented.
* [ ] Risks and assumptions identified.
* [ ] Document reviewed by project mentors and accepted prior to implementation of the major project milestones.

## Notes

The intent of this document is not only to describe the proposed implementation, but also to justify architectural decisions and provide a maintainable reference for future contributors to the project. It should evolve alongside the implementation, with significant design changes reflected in updates to the documentation.

Start by reviewing the repository's current implementation and requirements, then use docs/design.md or an equivalent location for the document. Cover the requested architecture, processing pipeline, data structures, AI/LLM design, error handling, dependencies, testing strategy, risks, and diagrams; done means the acceptance checklist is complete and the document is reviewed by project mentors.

Written by the indexing model from the issue text.