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