$ show aishwaryanr/awesome-generative-ai-guide
This repository is a comprehensive, practitioner-focused educational hub for generative AI. It is not a deployable software application or a traditional data pipeline, but a curated knowledge base that bridges theory and practice. The content is organized into free courses (Applied LLMs Mastery, Agentic AI, AI Evals), interview preparation guides for multiple AI roles (AI Engineer, AI PM, FDE, AI Strategist), system design case studies with runnable LangGraph code, and curated research updates (monthly paper lists, State of AI reports). The architecture is a hybrid of educational content and reusable code patterns, with the core value lying in its structured learning paths and conceptual frameworks. The repository's dominant character is educational content curation and research tracking. The code examples are explicitly demonstration-grade, designed to teach production-adjacent patterns like provider-agnostic LLM layers, hash-chained audit trails, and deterministic guardrails. These patterns are genuinely reusable and demonstrate strong engineering craftsmanship. The information architecture of the repo itself is excellent, making it easy for learners to navigate from foundational concepts to advanced system design. The repository is actively maintained with a massive community following. Its primary risk is the inherent rapid obsolescence of the AI field, which the maintainers mitigate by explicitly archiving outdated content and providing clear versioning. The code examples are not production-ready out of the box, but the patterns they teach are directly applicable to real-world systems. The research paper lists and interview questions require ongoing curation to stay current.
The LLM identified 4 files worth highlighting for reuse:
Provides a clean abstraction over LLM providers (Anthropic, OpenAI, etc.) that auto-detects the provider from environment variables and falls back to deterministic, offline-compatible logic. This pattern is highly reusable for decoupling from specific LLM providers and enabling reliable, fast unit testing without network calls or API costs.
Implements a tamper-evident audit trail by chaining log entries with SHA-256 hashes. Each entry includes the hash of the previous entry, making it computationally infeasible to alter historical records without detection. This is a production-grade pattern for compliance-heavy AI systems, directly reusable in any application requiring auditable decision logs.
Encodes business rules and safety policies as deterministic Python code that runs before or alongside model inference. This pattern separates 'knowable' logic (e.g., 'deny requests over $10k', 'mask PII') from model judgment, reducing hallucination risk and ensuring hard guarantees. The structure is clean, testable, and directly portable to other projects.
Demonstrates a rigorous evaluation workflow for a multi-step planning agent using BM25 retrieval, LLM-as-judge evaluation, and a trace-first methodology (observe -> discover -> measure -> improve). The custom metrics (SOP recall, plan alignment) and the structured evaluation loop are directly reusable for any team building and evaluating agentic systems.