Generative AI Systems for Newsroom Content Production

Summary

We build generative AI systems for newsroom content production. A system of this kind sits behind the newsroom, not in front of the reader. It ingests wire copy and other licensed source material and produces draft articles, headline variants, summaries and a scheduled news pulse that refreshes through the day from the current headline set. Output is bilingual where the audience requires it, and every artefact enters a review queue rather than a publishing pipeline.

The Challenge

The purpose is to compress the mechanical part of the news cycle, which is restating and repackaging source material, while leaving judgement, verification and attribution with editors. Hallucination is the dominant failure mode, and the mitigation is architectural rather than prompt-level. Wire licences restrict verbatim republication, so output that is too close to the source is as much a problem as output that is invented. Context windows fill faster than expected once several wire items are included, so selection and trimming logic matters more than model choice. Parameters that produce lively headlines produce unreliable body copy. Cost multiplies quietly when a pulse runs on a schedule. Above all, an approval gate that can be bypassed by any automated republication path is not a gate at all.

The Solution

Components

• Ingestion workers poll wire feeds and normalise every item into a canonical record with source identifier, licence terms, embargo time, topic classification and full text.
• A prompt and role registry holds system roles and templates as editable, versioned records so desk editors can change tone or structure without a deployment. Each generation stores the prompt version, model identifier and parameters used.
• A generation service calls a hosted model API, with retry, timeout and cost ceilings enforced at the service boundary rather than in application code.
• A scheduler drives the recurring pulse, selecting the current headline set and requesting a fresh summary on a fixed cadence.
• A review interface is where drafts are accepted, edited or discarded.

A relational store such as PostgreSQL holds source items, prompts, draft versions, approved final versions, translation pairs and token accounting.

Grounding, filtering and model behaviour

Generation is grounded: the source text travels in the request context and the model is instructed to work only from it, with anything unsupported omitted rather than inferred. Before that, wire data is filtered, because feeding raw firehose content into a model wastes context and amplifies duplication. Near-duplicate detection collapses the same story arriving from several agencies, embargo checks hold items until release, and topic classification routes an item to the right desk role. Editor feedback on which drafts were accepted, edited or discarded is valuable signal; using it as a preference ranking to tune selection is a reinforcement-style approach that improves style adherence without optimising toward a single brittle metric. Bilingual output is generated from the source material for each language rather than machine-translated from a finished draft, because idiom, name order and legal phrasing do not survive translation cleanly.

Editorial, rights and compliance

Originality checks run against the source before an item can be approved. Every published piece carries an audit trail linking prompt, model version, input items and output, so a correction can be traced. Disclosure labelling of AI-assisted content is a field on the article record, not a manual habit, and retention policies cover both generated drafts and licensed inputs.

How we build it

We start from the canonical source record and the review workflow, because those constrain everything else, then layer generation behind an interface that treats the model as a replaceable dependency. Prompts are treated as code: reviewed, versioned and regression tested against a fixed corpus so a wording change cannot silently degrade output quality.

What This Delivers

Editors get drafts, headline options and summaries waiting for judgement instead of a blank page, with the repetitive restating already done. Desks can retune tone and structure themselves, because prompts are editable records rather than code. Every piece is traceable back to its source items and the exact model and prompt that produced it, which is what makes a correction defensible. The newsroom keeps its licensing position intact and its editorial control unambiguous.

Technologies and Tools

Hosted large language model APIs behind a replaceable service interface; PostgreSQL for source items, prompts, draft and approved versions, translation pairs and token accounting; scheduled ingestion workers against wire feeds; near-duplicate detection and topic classification; a versioned prompt registry with regression testing against a fixed corpus.