07How do you get reliable structured output (JSON / function calls) from an LLM in production?▼medium★ EssentialOpenAIAnthropicMicrosoft2 repliesunlockedAgents and integrations live or die on the model returning valid, schema-conforming output. The signal is layering constrained decoding, schema validation, and retries, not hoping a prompt is enough. Here is the production-reliability answer.Open full answer →
127Write a JSON parser from scratch. Now make it handle the partial JSON an LLM streams mid-generation.▼hardNewOpenAIAnthropicDatabricks◆ premiumThe classic recursive-descent exercise with an applied-AI twist: the JSON your model streams is truncated mid-token for the entire generation. The signal is a clean strict parser plus a small repair layer, not a second parser. Here is the implementation.Open full answer →
30How do you query semi-structured data (JSON) in SQL, and when should you flatten vs keep it nested?▼mediumSnowflakeDatabricksAmazon2 replies◆ premiumModern warehouses store JSON natively, and querying it well separates strong data engineers from the rest. The signal is path access plus unnesting, and a clear call on when to flatten hot fields versus keep the schema-on-read flexibility.Open full answer →
56How do you query deeply nested JSON with arrays in SQL, and when do you flatten vs keep it nested?▼mediumSnowflakeDatabricksGoogle1 replies◆ premiumA flat path extract is easy; an array of objects three levels deep is where people freeze. The signal is LATERAL FLATTEN / UNNEST to explode arrays into rows and a clear rule for when to flatten vs query in place.Open full answer →
