Paweł Domański
256 lines
4.9 KiB
Markdown
256 lines
4.9 KiB
Markdown
---
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type: Note
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---
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# PROMPT: generowanie projektu Generative AI (LLM + RAG)
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Jasne — przygotuję Ci **gotowy, produkcyjny prompt**, który możesz wrzucić do **Claude Code / Copilot / dowolnego agenta**, żeby **automatycznie wygenerował projekt** w tej architekturze.
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Dam Ci wersję:
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- ✅ deterministyczną (minimalne „halucynacje”)
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- ✅ zgodną z Twoim stylem (DBA + SOP + operacyjność)
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- ✅ gotową do użycia w CLI (`claude -p`)
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***
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# ✅ PROMPT: generowanie projektu Generative AI (LLM + RAG)
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```text
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You are a senior software architect and platform engineer.
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Your task is to generate a complete, production-ready Generative AI project based on the following requirements.
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# CONTEXT
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The project must implement:
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- LLM abstraction (support OpenAI, Claude, local models)
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- Retrieval-Augmented Generation (RAG)
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- Data processing pipeline (chunking, tokenization, preprocessing)
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- Prompt management system
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- Inference engine
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- Testing and automation scripts
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- Container-ready (Docker)
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The system must be structured, maintainable, and suitable for enterprise environments.
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# REQUIREMENTS
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## 1. Project structure
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Create a full file tree exactly in this format:
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generative_ai_project/
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config/
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data/
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src/
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core/
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prompts/
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rag/
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processing/
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inference/
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tests/
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unit/
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integration/
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scripts/
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Include all files and implement real code (not placeholders).
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---
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## 2. Core architecture
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### src/core/
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Implement:
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- base_llm.py → abstract interface (send, generate, stream)
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- gpt_client.py → OpenAI client
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- claude_client.py → Claude client
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- local_llm.py → local model stub
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- model_factory.py → dynamic model selection based on config
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Requirements:
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- retries
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- timeout handling
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- structured logging
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---
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## 3. Prompt layer (src/prompts/)
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- templates.py → prompt templates (Jinja-like)
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- chain.py → multi-step chaining
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Requirements:
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- reusable prompt structure
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- system + user + context separation
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---
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## 4. RAG module (src/rag/)
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Implement:
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- embedder.py → embedding generation
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- retriever.py → similarity search (top-k)
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- vector_store.py → wrapper (FAISS or simple in-memory version)
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- indexer.py → indexing pipeline
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Requirements:
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- deterministic chunking
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- metadata support
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- clear interfaces between components
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---
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## 5. Data processing (src/processing/)
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Implement:
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- chunking.py → configurable chunk size + overlap
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- tokenizer.py → basic tokenizer abstraction
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- preprocessor.py → cleaning pipeline
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---
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## 6. Inference (src/inference/)
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Implement:
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- inference_engine.py → orchestration:
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user_query → retriever → prompt → LLM → response parser
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- response_parser.py → structured output (JSON-safe)
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---
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## 7. Config (config/)
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Create:
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- model_config.yaml
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- logging_config.yaml
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Requirements:
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- environment-aware (dev/prod)
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- no hardcoded secrets
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---
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## 8. Scripts (scripts/)
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Create:
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- setup_env.sh
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- run_tests.sh
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- build_embeddings.py
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- cleanup.py
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---
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## 9. Tests (tests/)
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Create:
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- unit tests for LLM clients and prompts
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- integration test for full RAG + inference flow
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---
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## 10. Operational requirements
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All code must:
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- include logging
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- include error handling
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- support debugging
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- avoid global state
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---
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## 11. Output format
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Return:
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1. Full directory tree
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2. Full code for each file
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3. Example config files
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4. Example usage (CLI or script)
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Do not skip files. Do not leave TODOs.
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---
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# CONSTRAINTS
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- Code must be Python
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- Clean architecture (separation of concerns)
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- No unnecessary dependencies
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- Ready to run locally
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---
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# BONUS
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If possible:
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- add simple CLI entrypoint
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- add example dataset
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- include README structure
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# GOAL
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The result must be a working skeleton that can be extended into production.
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Start now.
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```
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***
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# ✅ Jak tego użyć (praktycznie)
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## Claude Code (najlepsza opcja)
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```bash
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claude --bare -p "$(cat prompt.txt)"
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```
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albo:
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```bash
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claude --bare -p "<<<tu wklej prompt>>>"
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```
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***
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## 🔥 PRO TIP (dla Ciebie – advanced)
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Jeśli chcesz mieć **100% powtarzalności (bardzo w Twoim stylu)**:
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Dodaj do prompta:
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```text
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Use deterministic behavior:
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- avoid random examples
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- use fixed sample data
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- keep outputs stable
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```
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***
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# ✅ Wersja rozszerzona (dla Twojego systemu – OpenClaw / agents)
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Możesz to też użyć jako **skill/mission prompt**:
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```text
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MISSION: generate-genai-project
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INPUT:
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- project_name
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- llm_provider (gpt/claude/local)
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- use_rag (true/false)
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OUTPUT:
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- full repo structure
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- working code
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- scripts + tests
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CONSTRAINTS:
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- deterministic output
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- no placeholders
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- production-ready
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```
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***
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# Jeśli chcesz dalej
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Mogę Ci zrobić kolejne poziomy:
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✅ prompt do:
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- generowania **tylko RAG pipeline**
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- generowania **tylko inference engine**
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- generowania **runbooków + monitoring + alerts**
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✅ albo:
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- wersję pod **Twoje realne środowisko (Proxmox / on-prem / DB-backed RAG)**
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- integrację z **Splunk / ServiceNow / Agentami (MCC/APOLLO)**
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Powiedz kierunek → zrobię Ci to już pod konkretne wdrożenie 👍
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