AI/LLM Skill

PURPOSE

LLM applications, prompt engineering, and AI system design.

CORE COMPETENCIES

Prompt Engineering:
├── Zero-shot, few-shot
├── Chain-of-thought
├── System prompts
└── Output formatting

RAG (Retrieval-Augmented Generation):
├── Document chunking
├── Embedding models
├── Vector databases
├── Retrieval strategies
└── Context window management

LLM Frameworks:
├── LangChain
├── LlamaIndex
├── Anthropic SDK
└── OpenAI API

CODE PATTERNS

RAG Pipeline

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI

# Initialize
embeddings = OpenAIEmbeddings()
vectorstore = Pinecone.from_existing_index("docs", embeddings)
llm = ChatOpenAI(model="gpt-4", temperature=0)

# RAG chain
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=vectorstore.as_retriever(
        search_kwargs={"k": 5}
    ),
    return_source_documents=True
)

result = qa_chain({"query": "How does X work?"})

Retry with Backoff

from tenacity import retry, stop_after_attempt, wait_exponential

@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=1, max=10)
)
def call_llm(prompt: str) -> str:
    return client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    ).choices[0].message.content

TROUBLESHOOTING

BEST PRACTICES

1. Use structured outputs (JSON mode)
2. Implement proper error handling
3. Add observability (LangSmith, Arize)
4. Cache responses when possible
5. Use streaming for long responses
6. Evaluate with diverse test sets