Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
npxskills add davila7/claude-code-templates--skill llm-app-patternsLoading…
Production-ready patterns for building LLM applications, inspired by Dify and industry best practices.
Use this skill when:
RAG (Retrieval-Augmented Generation) grounds LLM responses in your data.
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│ Ingest │────▶│ Retrieve │────▶│ Generate │
│ Documents │ │ Context │ │ Response │
└─────────────┘ └─────────────┘ └─────────────┘
│ │ │
▼ ▼ ▼
┌─────────┐ ┌───────────┐ ┌───────────┐
│ Chunking│ │ Vector │ │ LLM │
│Embedding│ │ Search │ │ + Context│
└─────────┘ └───────────┘ └───────────┘
# Chunking strategies
class ChunkingStrategy:
# Fixed-size chunks (simple but may break context)
FIXED_SIZE = "fixed_size" # e.g., 512 tokens
# Semantic chunking (preserves meaning)
SEMANTIC = "semantic" # Split on paragraphs/sections
# Recursive splitting (tries multiple separators)
RECURSIVE = "recursive" # ["\n\n", "\n", " ", ""]
# Document-aware (respects structure)
DOCUMENT_AWARE = "document_aware" # Headers, lists, etc.
# Recommended settings
CHUNK_CONFIG = {
"chunk_size": 512, # tokens
"chunk_overlap": 50, # token overlap between chunks
"separators": ["\n\n", "\n", ". ", " "],
}
# Vector database selection
VECTOR_DB_OPTIONS = {
"pinecone": {
"use_case": "Production, managed service",
"scale": "Billions of vectors",
"features": ["Hybrid search", "Metadata filtering"]
},
"weaviate": {
"use_case": "Self-hosted, multi-modal",
"scale": "Millions of vectors",
"features": ["GraphQL API", "Modules"]
},
"chromadb": {
"use_case": "Development, prototyping",
"scale": "Thousands of vectors",
"features": ["Simple API", "In-memory option"]
},
"pgvector": {
"use_case": "Existing Postgres infrastructure",
"scale": "Millions of vectors",
"features": ["SQL integration", "ACID compliance"]
}
}
# Embedding model selection
EMBEDDING_MODELS = {
"openai/text-embedding-3-small": {
"dimensions": 1536,
"cost": "$0.02/1M tokens",
"quality": "Good for most use cases"
},
"openai/text-embedding-3-large": {
"dimensions": 3072,
"cost": "$0.13/1M tokens",
"quality": "Best for complex queries"
},
"local/bge-large": {
"dimensions": 1024,
"cost": "Free (compute only)",
"quality": "Comparable to OpenAI small"
}
}
# Basic semantic search
def semantic_search(query: str, top_k: int = 5):
query_embedding = embed(query)
results = vector_db.similarity_search(
query_embedding,
top_k=top_k
)
return results
# Hybrid search (semantic + keyword)
def hybrid_search(query: str, top_k: int = 5, alpha: float = 0.5):
"""
alpha=1.0: Pure semantic
alpha=0.0: Pure keyword (BM25)
alpha=0.5: Balanced
"""
semantic_results = vector_db.similarity_search(query)
keyword_results = bm25_search(query)
# Reciprocal Rank Fusion
return rrf_merge(semantic_results, keyword_results, alpha)
# Multi-query retrieval
def multi_query_retrieval(query: str):
"""Generate multiple query variations for better recall"""
queries = llm.generate_query_variations(query, n=3)
all_results = []
for q in queries:
all_results.extend(semantic_search(q))
return deduplicate(all_results)
# Contextual compression
def compressed_retrieval(query: str):
"""Retrieve then compress to relevant parts only"""
docs = semantic_search(query, top_k=10)
compressed = llm.extract_relevant_parts(docs, query)
return compressed
RAG_PROMPT_TEMPLATE = """
Answer the user's question based ONLY on the following context.
If the context doesn't contain enough information, say "I don't have enough information to answer that."
Context:
{context}
Question: {question}
Answer:"""
def generate_with_rag(question: str):
# Retrieve
context_docs = hybrid_search(question, top_k=5)
context = "\n\n".join([doc.content for doc in context_docs])
# Generate
prompt = RAG_PROMPT_TEMPLATE.format(
context=context,
question=question
)
response = llm.generate(prompt)
# Return with citations
return {
"answer": response,
"sources": [doc.metadata for doc in context_docs]
}
Thought: I need to search for information about X
Action: search("X")
Observation: [search results]
Thought: Based on the results, I should...
Action: calculate(...)
Observation: [calculation result]
Thought: I now have enough information
Action: final_answer("The answer is...")
REACT_PROMPT = """
You are an AI assistant that can use tools to answer questions.
Available tools:
{tools_description}
Use this format:
Thought: [your reasoning about what to do next]
Action: [tool_name(arguments)]
Observation: [tool result - this will be filled in]
... (repeat Thought/Action/Observation as needed)
Thought: I have enough information to answer
Final Answer: [your final response]
Question: {question}
"""
class ReActAgent:
def __init__(self, tools: list, llm):
self.tools = {t.name: t for t in tools}
self.llm = llm
self.max_iterations = 10
def run(self, question: str) -> str:
prompt = REACT_PROMPT.format(
tools_description=self._format_tools(),
question=question
)
for _ in range(self.max_iterations):
response = self.llm.generate(prompt)
if "Final Answer:" in response:
return self._extract_final_answer(response)
action = self._parse_action(response)
observation = self._execute_tool(action)
prompt += f"\nObservation: {observation}\n"
return "Max iterations reached"
# Define tools as functions with schemas
TOOLS = [
{
"name": "search_web",
"description": "Search the web for current information",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "Search query"
}
},
"required": ["query"]
}
},
{
"name": "calculate",
"description": "Perform mathematical calculations",
"parameters": {
"type": "object",
"properties": {
"expression": {
"type": "string",
"description": "Math expression to evaluate"
}
},
"required": ["expression"]
}
}
]
class FunctionCallingAgent:
def run(self, question: str) -> str:
messages = [{"role": "user", "content": question}]
while True:
response = self.llm.chat(
messages=messages,
tools=TOOLS,
tool_choice="auto"
)
if response.tool_calls:
for tool_call in response.tool_calls:
result = self._execute_tool(
tool_call.name,
tool_call.arguments
)
messages.append({
"role": "tool",
"tool_call_id": tool_call.id,
"content": str(result)
})
else:
return response.content
class PlanAndExecuteAgent:
"""
1. Create a plan (list of steps)
2. Execute each step
3. Replan if needed
"""
def run(self, task: str) -> str:
# Planning phase
plan = self.planner.create_plan(task)
# Returns: ["Step 1: ...", "Step 2: ...", ...]
results = []
for step in plan:
# Execute each step
result = self.executor.execute(step, context=results)
results.append(result)
# Check if replan needed
if self._needs_replan(task, results):
new_plan = self.planner.replan(
task,
completed=results,
remaining=plan[len(results):]
)
plan = new_plan
# Synthesize final answer
return self.synthesizer.summarize(task, results)
class AgentTeam:
"""
Specialized agents collaborating on complex tasks
"""
def __init__(self):
self.agents = {
"researcher": ResearchAgent(),
"analyst": AnalystAgent(),
"writer": WriterAgent(),
"critic": CriticAgent()
}
self.coordinator = CoordinatorAgent()
def solve(self, task: str) -> str:
# Coordinator assigns subtasks
assignments = self.coordinator.decompose(task)
results = {}
for assignment in assignments:
agent = self.agents[assignment.agent]
result = agent.execute(
assignment.subtask,
context=results
)
results[assignment.id] = result
# Critic reviews
critique = self.agents["critic"].review(results)
if critique.needs_revision:
# Iterate with feedback
return self.solve_with_feedback(task, results, critique)
return self.coordinator.synthesize(results)
class PromptTemplate:
def __init__(self, template: str, variables: list[str]):
self.template = template
self.variables = variables
def format(self, **kwargs) -> str:
# Validate all variables provided
missing = set(self.variables) - set(kwargs.keys())
if missing:
raise ValueError(f"Missing variables: {missing}")
return self.template.format(**kwargs)
def with_examples(self, examples: list[dict]) -> str:
"""Add few-shot examples"""
example_text = "\n\n".join([
f"Input: {ex['input']}\nOutput: {ex['output']}"
for ex in examples
])
return f"{example_text}\n\n{self.template}"
# Usage
summarizer = PromptTemplate(
template="Summarize the following text in {style} style:\n\n{text}",
variables=["style", "text"]
)
prompt = summarizer.format(
style="professional",
text="Long article content..."
)
class PromptRegistry:
def __init__(self, db):
self.db = db
def register(self, name: str, template: str, version: str):
"""Store prompt with version"""
self.db.save({
"name": name,
"template": template,
"version": version,
"created_at": datetime.now(),
"metrics": {}
})
def get(self, name: str, version: str = "latest") -> str:
"""Retrieve specific version"""
return self.db.get(name, version)
def ab_test(self, name: str, user_id: str) -> str:
"""Return variant based on user bucket"""
variants = self.db.get_all_versions(name)
bucket = hash(user_id) % len(variants)
return variants[bucket]
def record_outcome(self, prompt_id: str, outcome: dict):
"""Track prompt performance"""
self.db.update_metrics(prompt_id, outcome)
class PromptChain:
"""
Chain prompts together, passing output as input to next
"""
def __init__(self, steps: list[dict]):
self.steps = steps
def run(self, initial_input: str) -> dict:
context = {"input": initial_input}
results = []
for step in self.steps:
prompt = step["prompt"].format(**context)
output = llm.generate(prompt)
# Parse output if needed
if step.get("parser"):
output = step["parser"](output)
context[step["output_key"]] = output
results.append({
"step": step["name"],
"output": output
})
return {
"final_output": context[self.steps[-1]["output_key"]],
"intermediate_results": results
}
# Example: Research → Analyze → Summarize
chain = PromptChain([
{
"name": "research",
"prompt": "Research the topic: {input}",
"output_key": "research"
},
{
"name": "analyze",
"prompt": "Analyze these findings:\n{research}",
"output_key": "analysis"
},
{
"name": "summarize",
"prompt": "Summarize this analysis in 3 bullet points:\n{analysis}",
"output_key": "summary"
}
])
LLM_METRICS = {
# Performance
"latency_p50": "50th percentile response time",
"latency_p99": "99th percentile response time",
"tokens_per_second": "Generation speed",
# Quality
"user_satisfaction": "Thumbs up/down ratio",
"task_completion": "% tasks completed successfully",
"hallucination_rate": "% responses with factual errors",
# Cost
"cost_per_request": "Average $ per API call",
"tokens_per_request": "Average tokens used",
"cache_hit_rate": "% requests served from cache",
# Reliability
"error_rate": "% failed requests",
"timeout_rate": "% requests that timed out",
"retry_rate": "% requests needing retry"
}
import logging
from opentelemetry import trace
tracer = trace.get_tracer(__name__)
class LLMLogger:
def log_request(self, request_id: str, data: dict):
"""Log LLM request for debugging and analysis"""
log_entry = {
"request_id": request_id,
"timestamp": datetime.now().isoformat(),
"model": data["model"],
"prompt": data["prompt"][:500], # Truncate for storage
"prompt_tokens": data["prompt_tokens"],
"temperature": data.get("temperature", 1.0),
"user_id": data.get("user_id"),
}
logging.info(f"LLM_REQUEST: {json.dumps(log_entry)}")
def log_response(self, request_id: str, data: dict):
"""Log LLM response"""
log_entry = {
"request_id": request_id,
"completion_tokens": data["completion_tokens"],
"total_tokens": data["total_tokens"],
"latency_ms": data["latency_ms"],
"finish_reason": data["finish_reason"],
"cost_usd": self._calculate_cost(data),
}
logging.info(f"LLM_RESPONSE: {json.dumps(log_entry)}")
# Distributed tracing
@tracer.start_as_current_span("llm_call")
def call_llm(prompt: str) -> str:
span = trace.get_current_span()
span.set_attribute("prompt.length", len(prompt))
response = llm.generate(prompt)
span.set_attribute("response.length", len(response))
span.set_attribute("tokens.total", response.usage.total_tokens)
return response.content
class LLMEvaluator:
"""
Evaluate LLM outputs for quality
"""
def evaluate_response(self,
question: str,
response: str,
ground_truth: str = None) -> dict:
scores = {}
# Relevance: Does it answer the question?
scores["relevance"] = self._score_relevance(question, response)
# Coherence: Is it well-structured?
scores["coherence"] = self._score_coherence(response)
# Groundedness: Is it based on provided context?
scores["groundedness"] = self._score_groundedness(response)
# Accuracy: Does it match ground truth?
if ground_truth:
scores["accuracy"] = self._score_accuracy(response, ground_truth)
# Harmfulness: Is it safe?
scores["safety"] = self._score_safety(response)
return scores
def run_benchmark(self, test_cases: list[dict]) -> dict:
"""Run evaluation on test set"""
results = []
for case in test_cases:
response = llm.generate(case["prompt"])
scores = self.evaluate_response(
question=case["prompt"],
response=response,
ground_truth=case.get("expected")
)
results.append(scores)
return self._aggregate_scores(results)
import hashlib
from functools import lru_cache
class LLMCache:
def __init__(self, redis_client, ttl_seconds=3600):
self.redis = redis_client
self.ttl = ttl_seconds
def _cache_key(self, prompt: str, model: str, **kwargs) -> str:
"""Generate deterministic cache key"""
content = f"{model}:{prompt}:{json.dumps(kwargs, sort_keys=True)}"
return hashlib.sha256(content.encode()).hexdigest()
def get_or_generate(self, prompt: str, model: str, **kwargs) -> str:
key = self._cache_key(prompt, model, **kwargs)
# Check cache
cached = self.redis.get(key)
if cached:
return cached.decode()
# Generate
response = llm.generate(prompt, model=model, **kwargs)
# Cache (only cache deterministic outputs)
if kwargs.get("temperature", 1.0) == 0:
self.redis.setex(key, self.ttl, response)
return response
import time
from tenacity import retry, wait_exponential, stop_after_attempt
class RateLimiter:
def __init__(self, requests_per_minute: int):
self.rpm = requests_per_minute
self.timestamps = []
def acquire(self):
"""Wait if rate limit would be exceeded"""
now = time.time()
# Remove old timestamps
self.timestamps = [t for t in self.timestamps if now - t < 60]
if len(self.timestamps) >= self.rpm:
sleep_time = 60 - (now - self.timestamps[0])
time.sleep(sleep_time)
self.timestamps.append(time.time())
# Retry with exponential backoff
@retry(
wait=wait_exponential(multiplier=1, min=4, max=60),
stop=stop_after_attempt(5)
)
def call_llm_with_retry(prompt: str) -> str:
try:
return llm.generate(prompt)
except RateLimitError:
raise # Will trigger retry
except APIError as e:
if e.status_code >= 500:
raise # Retry server errors
raise # Don't retry client errors
class LLMWithFallback:
def __init__(self, primary: str, fallbacks: list[str]):
self.primary = primary
self.fallbacks = fallbacks
def generate(self, prompt: str, **kwargs) -> str:
models = [self.primary] + self.fallbacks
for model in models:
try:
return llm.generate(prompt, model=model, **kwargs)
except (RateLimitError, APIError) as e:
logging.warning(f"Model {model} failed: {e}")
continue
raise AllModelsFailedError("All models exhausted")
# Usage
llm_client = LLMWithFallback(
primary="gpt-4-turbo",
fallbacks=["gpt-3.5-turbo", "claude-3-sonnet"]
)
| Pattern | Use When | Complexity | Cost |
|---|---|---|---|
| Simple RAG | FAQ, docs search | Low | Low |
| Hybrid RAG | Mixed queries | Medium | Medium |
| ReAct Agent | Multi-step tasks | Medium | Medium |
| Function Calling | Structured tools | Low | Low |
| Plan-Execute | Complex tasks | High | High |
| Multi-Agent | Research tasks | Very High | Very High |
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).