How to Implement Structured Output with Local LLMs
LLM Applications How to Implement Structured Output with Local LLMs Why use it? Shuai Guo Aug 9, 2026 9 min read Share Generated by GPT-Image 2 For building LLM applications, local LLMs are an attractive option. They allow us to keep our sensitive data and reduce our dependency on cloud APIs.
- ▪LLM Applications How to Implement Structured Output with Local LLMs Why use it?
- ▪Shuai Guo Aug 9, 2026 9 min read Share Generated by GPT-Image 2 For building LLM applications, local LLMs are an attractive option.
- ▪They allow us to keep our sensitive data and reduce our dependency on cloud APIs.
Towards Data Science files mainly under ai. We currently carry 128 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Towards Data Science |
| Canonical URL | https://towardsdatascience.com/structured-output-with-local-llms/ |
| Publication time | Sun, 09 Aug 2026 13:00:00 +0000 |
| Retrieval time | 2026-08-09T13:05:47.595Z |
| Last seen | 2026-08-09T13:05:47.595Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | d4riCGYVsm4t · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
LLM Applications How to Implement Structured Output with Local LLMs Why use it? How to implement it? What can we do when it fails? Shuai Guo Aug 9, 2026 9 min read Share Generated by GPT-Image 2 For building LLM applications, local LLMs are an attractive option. They allow us to keep our sensitive data and reduce our dependency on cloud APIs. However, running the model locally is only the first step. In a practical application, the local LLM is usually part of a larger workflow. This means its responses often need to be consumed by another component. In those situations, free-form text can be very difficult to work with. We want the output to follow some predictable structures. That’s exactly what Structured Output is for.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
Discussion
0 commentsMore from Towards Data Science
