llm-app-patterns

name	llm-app-patterns
type	reference
description	Provides architectural patterns for LLM-powered applications and AI assistants, including prompt engineering, RAG, agent loops, conversation management, and evaluation. Use when building AI-based features, chatbots, or complex AI system architectures.
paths	["**/*.py","**/*.ts","**/openai*","**/anthropic*","**/langchain*","**/chatbot*","**/assistant*"]
effort	3
allowed-tools	Read, Glob, Grep, Write, Edit, Bash
user-invocable	true
when_to_use	When designing LLM applications, building AI assistants/chatbots, implementing RAG pipelines, or setting up agent architectures.

LLM Application & AI Assistant Patterns

Resources

• resources/implementation-playbook.md for detailed patterns and examples.

Architecture decision matrix

Pattern	Use when	Cost
Simple RAG	FAQ, docs Q&A	Low
Hybrid RAG (semantic + BM25)	Mixed query types	Medium
Function calling	Structured tool use	Low
ReAct agent	Multi-step reasoning	Medium
Plan-and-execute	Complex decomposable tasks	High
Multi-agent	Research, critique-refine	Very High

RAG: critical config numbers

CHUNK_CONFIG = {
    "chunk_size": 512,       # tokens — sweet spot for most docs
    "chunk_overlap": 50,     # prevents context loss at boundaries
    "separators": ["\n\n", "\n", ". ", " "],
}
# Hybrid search alpha: 1.0=semantic only, 0.0=BM25 only, 0.5=balanced

RAG: retrieval strategies

# Basic: semantic search
results = vector_db.similarity_search(embed(query), top_k=5)

# Better: hybrid (semantic + keyword via RRF)
def hybrid_search(query, alpha=0.5):
    return rrf_merge(vector_db.search(query), bm25_search(query), alpha)

# Best for recall: multi-query (3 variations, deduplicate)
queries = llm.generate_variations(query, n=3)
results = deduplicate([semantic_search(q) for q in queries])

RAG: generation prompt template

RAG_PROMPT = """Answer based ONLY on the context below.
If insufficient, say "I don't have enough information."

Context: {context}
Question: {question}
Answer:"""

Agent: function calling loop

messages = [{"role": "user", "content": question}]
while True:
    response = llm.chat(messages=messages, tools=TOOLS, tool_choice="auto")
    if not response.tool_calls:
        return response.content
    for call in response.tool_calls:
        result = execute_tool(call.name, call.arguments)
        messages.append({"role": "tool", "tool_call_id": call.id, "content": str(result)})

Production: caching (only temperature=0 responses)

def get_or_generate(prompt, model, **kwargs):
    deterministic = kwargs.get("temperature", 1.0) == 0
    if deterministic:
        key = sha256(f"{model}:{prompt}:{json.dumps(kwargs, sort_keys=True)}")
        if cached := redis.get(key): return cached
    response = llm.generate(prompt, model=model, **kwargs)
    if deterministic: redis.setex(key, 3600, response)
    return response

Production: retry + fallback

from tenacity import retry, wait_exponential, stop_after_attempt

@retry(wait=wait_exponential(multiplier=1, min=4, max=60), stop=stop_after_attempt(5))
def call_llm(prompt): return llm.generate(prompt)

# Fallback chain
for model in [primary] + fallbacks:
    try: return llm.generate(prompt, model=model)
    except (RateLimitError, APIError): continue

LLMOps: key metrics

Latency : p50, p99 response time
Quality : satisfaction (thumbs), task completion %, hallucination rate
Cost    : cost_per_request, tokens_per_request, cache_hit_rate
Health  : error_rate, timeout_rate, retry_rate

Embedding model selection

Model	Dims	Cost	Use
text-embedding-3-small	1536	$0.02/1M	Most cases
text-embedding-3-large	3072	$0.13/1M	High accuracy
bge-large (local)	1024	Free	Self-hosted