LLM Systems and Python Integration Assessment

Retry immediately in a tight loop until the request succeeds

Switch to a different model automatically on every failure

Add exponential backoff with jitter, honor Retry-After headers, cap retries, and degrade gracefully if limits persist

Increase max tokens to reduce error frequency

Remove timeouts so the request can eventually complete

The client is not consuming the full stream or is closing the connection early

The model refuses to answer certain prompts

The database is experiencing read latency

Increase the number of retrieved documents until relevant results appear

Rebuild the entire index using a larger LLM

Inspect chunk size, overlap strategy, and embedding model choice, then evaluate retrieval with a small labeled query set

Remove retrieval and rely on the base model

Tell the model to ignore the request and hope it complies

Treat all tool inputs as untrusted, allowlist tools and data access, keep secrets out of the prompt context, and sandbox retrieval

Increase max tokens so the model can explain why it is safe

Cache repeated prompts, stream responses, and route simple queries to a smaller model

Reduce response length globally for all requests

Parse free-form text and attempt to extract JSON with regex

Increase temperature to improve formatting

Ask the model twice and compare responses

Use schema-constrained output, validate responses against the schema, and retry with a repair prompt if validation fails

Add explicit timeouts, circuit breakers, and request cancellation handling

Increase token limits to avoid partial responses

Add a disclaimer stating that answers may be inaccurate

Rely on stricter prompt instructions and system messages

Require citations tied to retrieved documents and refuse when no authorized sources are retrieved, with generation blocked if retrieval is empty

Use a larger model to reduce hallucinations

Increase context length to include more policy text

Store all conversations indefinitely for future training

Encrypt stored data, minimize retained fields, enforce access controls, apply defined retention policies, and avoid storing sensitive content unless strictly necessary

Share user history across tenants to improve model accuracy

Avoid storing any data even when necessary for functionality

Increase model temperature for better alignment

Add a verification layer that ensures citations map to retrieved passages and suppress or refuse citation output when retrieval is empty or mismatched

Ignore the issue as a known model limitation

LLM Systems and Python Integration Assessment

When calling an LLM API from Python, you encounter intermittent 429 and 503 errors. What is the most appropriate production approach?