LoopyLuci Generative AI Patterns
Implementing generative AI applications — from LLM-based patterns (RAG, agents, chains) through diffusion models, structured generation, and evaluation frameworks.
When to Use
- Building LLM-powered applications
- Implementing RAG (Retrieval-Augmented Generation)
- Building AI agents with tools and memory
- Generating images, code, or structured data
- Evaluating and monitoring GenAI outputs
GenAI Application Patterns
GENAI_PATTERNS = {
'rag': 'Retrieval-Augmented Generation — ground LLM in external knowledge',
'agent': 'Tool-using LLM that plans, acts, and observes',
'chain': 'Composed LLM calls — sequential or parallel with intermediate outputs',
'structured': 'LLM outputs structured data (JSON, schema) from natural language',
'multi_modal': 'Generate or understand across text, image, audio, video',
'evaluation': 'LLM-as-judge, assertion-based, or human evaluation',
}
class GenAIApplication:
"""Pattern-based generative AI application builder."""
def rag_chain(self, vector_store, llm, query: str) -> str:
docs = vector_store.similarity_search(query, k=3)
context = '\n'.join(d.page_content for d in docs)
prompt = f"Answer using context:\n{context}\n\nQuestion: {query}"
return llm.invoke(prompt)
def structured_output(self, llm, text: str, schema: Dict) -> Dict:
prompt = f"Extract {schema} from: {text}. Return as valid JSON."
result = llm.invoke(prompt)
return json.loads(result)
Verification Checklist
- Application pattern chosen (RAG, agent, chain, structured, multi-modal)
- LLM provider and model selected
- Vector store configured (if RAG)
- Prompt engineering optimized for the pattern
- Evaluation framework in place (LLM-as-judge, assertions)
- Cost and latency measured per inference
- Guardrails for safety, accuracy, and bias