How do you build content generation with RAG?

Key facts

Retrieval	POST /v1/embeddings with plugsky-embed over brand guides, product facts and approved content
Generation	POST /v1/chat/completions with retrieved evidence and citation requirements
Voice consistency	Retrieve style examples and banned-phrase lists per channel
Fact traceability	Store source ids with each draft section for editorial review
Pricing model	Flat monthly self-serve plans with unlimited fair-use usage
RAG status	RAG is live; embeddings plus chat completions are the building blocks
Governance	Audit logs tie drafts to retrieved evidence and model version
Roadmap	Batch ingestion is coming soon; schedule index refreshes yourself

TL;DR

• Retrieve facts and voice examples before drafting, not after a review complaint.
• Index approved content only; drafts are not sources of truth.
• Require inline source references so reviewers can verify claims quickly.
• Feed editorial corrections back into the index to close the loop.
• Deduplicate near-identical approved pieces so retrieval does not produce derivative copy.

How it works, step by step

1. Collect approved source material: product facts, policies, style guides, terminology and published articles.
2. Tag each item with channel, audience, approval date and owner before indexing.
3. Chunk and embed with plugsky-embed, storing source metadata and version for every chunk.
4. Draft with retrieved evidence in the prompt and require references to source ids.
5. Run a verification pass that flags claims without a retrieved source, then edit for voice.
6. After publication, index the approved final version and retire superseded sources.

Try it yourself

Open the system prompt generator →

Why content needs grounding

Three content problems are really retrieval problems:

• Product facts change: pricing, features and policies evolve, and model memory lags. Retrieved facts are current by construction.
• Voice is specific: tone, terminology and banned phrases live in your style guides and approved pieces, not in general training data.
• Claims need review: reviewers verify faster when every statement carries a source, and reviewers are the bottleneck in most pipelines.

Grounding makes review faster and turns corrections into index updates instead of prompt folklore.

Building the content index

Index only material you are willing to cite:

• Product facts: specifications, pricing references, feature status and support policies.
• Style guides: terminology, tone rules, formatting conventions and banned phrases per channel.
• Approved content: published articles and pages that show the voice in practice.
• Source documents: research, filings and official statements that support factual claims.

Tag every entry with channel, audience, approval date and owner. Deduplicate overlapping articles, and retire items when a newer version supersedes them, so retrieval never cites stale copy as if it were current.

Implementation and evaluation

The pipeline retrieves, drafts, verifies and learns:

1. Build the brief, then retrieve facts, voice examples and relevant prior content.
2. Draft with an instruction to reference source ids inline.
3. Flag any claim without a matched source and either remove it or send it for research.
4. Edit for structure and length without adding claims.
5. Publish, then index the approved final version and retire superseded entries.

Evaluate retrieval hit rate, share of supported claims, editorial edit distance and how often reviewers reject a draft for a missing or stale source. The last metric points directly at index maintenance work.

Limitations

RAG improves grounding but changes nothing about strategy or originality:

• A weak content strategy produces well-sourced but pointless articles; retrieval cannot supply judgment.
• Retrieving near-duplicate approved pieces can push drafts toward derivative writing. Deduplicate the index and rotate examples.
• Index freshness is ongoing work: every product change needs a corresponding content update.
• Plagiarism and licensing checks remain human or tool-based responsibilities, especially when source documents come from third parties.
• Batch ingestion is coming soon, so initial large indexes need rate-limited jobs you run yourself.

Honest comparison

Capability	Grounded content pipeline	Prompt-only generation	Manual writing with research
Facts	Retrieved from approved sources	Model memory	Researcher effort
Voice	Style examples and terminology index	Prompt description	Native
Claim review	Source ids attached to each statement	Ad hoc	Fact-checking process
Updates	Re-index changed facts	Prompt rewrite	Manual rewrite
Scale	High with human publish gate	High with high rework	Limited by staff

Frequently asked questions

What should I index for content generation?

Product facts, policies, style guides, terminology lists and approved published content, each tagged with channel, audience and approval date.

How do I keep brand voice consistent?

Retrieve style examples and banned-phrase lists per channel and include them in the draft prompt. A style guide alone rarely communicates tone.

Can retrieval prevent all factual errors?

No. It prevents errors where you have a source. Claims without a retrieved source should be flagged and either researched or removed.

How often should the index refresh?

On every product or policy change for affected entries, plus a periodic review of approval dates to retire stale content.

Will this make everything sound the same?

If your index contains many near-duplicate pieces, retrieval can push writing toward sameness. Deduplicate and vary the retrieved examples by angle.

Does Plugsky generate images for articles?

Not yet — image endpoints are coming soon. This pipeline covers text; visual assets need separate tooling today.

Can I build the index on the free plan?

Yes. Embeddings are live, the free plan includes two free models with no card, and the 14-day full-access trial lets you evaluate drafting aliases on your content.