# honestsoul/generative_ai_project

A structured template for building robust generative AI applications

Repository: https://github.com/honestsoul/generative_ai_project
Canonical: https://ross.abutalabs.com/products/generative_ai_project
Language: Python
License Family: other
Last push: 2026-07-06T15:36:19+00:00

## Health v2 (maintenance only)
Score: 62/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 91, release rhythm 35, longevity 43
- inputs: {"age_days": 602, "days_push": 58, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1047, forks 457 (observed 2026-08-28T04:03:21.873506+00:00)

## What it is
A Python project template providing a modular structure for building generative AI applications, with pre-built LLM clients for Claude and GPT. It includes prompt engineering utilities, rate limiting, token counting, response caching, and error handling out of the box.

## Use cases
- scaffold a new generative AI project
- structure a Python LLM application
- manage prompts and templates for LLM apps
- add rate limiting and caching to LLM API calls
- compare Claude and GPT clients in one codebase
- experiment with prompt chaining in notebooks

## When to choose
- starting a new LLM-powered Python project and wanting a proven structure
- you need multi-provider LLM support with shared utilities
- you want built-in rate limiting, caching, and token management

## When to avoid
- you already use a full framework like LangChain or LlamaIndex
- you need a production-ready service rather than a template
- you require a permissive license - the repo has none

## Facets
- artifact type: learning-resource
- maturity: active
- function: llm-inference, prompt-engineering, caching, rate-limiting, logging, error-handling
- domain: large-language-models, artificial-intelligence, developer-tools
- platform: python, cross-platform
- tags: project-template, llm-clients, prompt-templates, token-management, starter-kit, boilerplate

## Member repositories
- honestsoul/generative_ai_project (main) score 62

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:21.873506+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T08:33:38.335466+00:00, confidence not recorded.
  - readme: https://github.com/honestsoul/generative_ai_project (fetched 2026-08-28T04:03:21.873506+00:00, sha 43f8af23a110)
- Data as of 2026-08-30T08:39:29.467469+00:00.