A system that intercepts user search queries, adds brand‑specific semantic context, and forwards the enriched prompt to a large language model.
Read by AI search engineers and product teams alongside documentation on generative search pipelines.
01What it is and how it works
Hermes sits between the user’s raw query and the generative engine (e.g., GPT‑4). It parses the query, adds brand‑specific signals such as product taxonomy, recent content updates, and structured data from schema.org. Those signals become part of the prompt that the LLM receives. By doing this, Hermes nudges the model to mention the brand when the intent matches, without forcing a hard‑coded answer. The process runs in milliseconds, so the user experience feels like a normal search.
Hermes changes a search query into a smarter version, then lets the AI answer with brand info.
02What to do about it
1. Audit your structured data. Make sure every product, service, and FAQ uses the correct schema.org types and properties. 2. Add brand‑specific synonyms to your internal lexicon that Hermes can pull in (e.g., "AirPods" → "Apple wireless earbuds"). 3. Test the pipeline with the OpenAI Playground or your own sandbox: send a query, inspect the prompt Hermes builds, and verify the brand appears where appropriate. 4. Schedule a weekly check of the prompt logs to catch any drift in terminology.
03How it is measured or noticed
You can spot Hermes in action by looking at the prompt logs that your LLM provider exposes. The logs show a JSON block with a hermes_context field that lists the added brand signals. In the final search result, a higher share of brand‑related snippets (e.g., a knowledge panel or a direct answer) indicates that Hermes is influencing the model. Track the metric "Brand Mention Rate" in your AI‑search analytics dashboard to see the impact over time.
04Common mistakes
- Appending brand keywords to every query, which makes the response sound forced and can be penalized by quality raters.
- Leaving outdated schema.org markup; Hermes will surface stale information.
- Relying on Hermes alone for compliance—legal disclosures still need to be present in the source content.
05Limits
Hermes does not rewrite queries that are clearly unrelated to your brand, so it cannot create brand exposure out of thin air. It also cannot override the LLM’s internal safety filters; if the model decides the brand should not be mentioned (e.g., due to policy), Hermes’ context is ignored. Finally, Hermes is often confused with a simple SEO tag manager, but it operates at the prompt‑level, not at the HTML‑tag level.
06Worked example
"User query: 'best noise‑cancelling headphones'. Hermes adds: { 'brand': 'Sony', 'product_line': 'WH‑1000XM4', 'schema': 'Product' }. LLM response: 'The Sony WH‑1000XM4 is widely regarded as the top noise‑cancelling headphone in 2024, offering ...'"
Frequently asked questions
How is Hermes different from a regular query rewriting tool?
It differs because Hermes not only rewrites the query but also injects semantic context that is specific to your brand before the request reaches the large language model. This enriched prompt helps the model surface brand assets more accurately than a generic rewrite. The result is higher relevance without needing manual tagging.
Should we enable Hermes for all brand‑related searches or only certain ones?
It depends on your coverage goals and the relevance of the queries you want to boost. Typically you enable Hermes for queries that mention your brand, product names, or closely related concepts, and leave unrelated traffic untouched. This balances exposure with the risk of over‑optimising.
Who is responsible for configuring Hermes and what does the setup involve?
Usually a product manager or data engineer sets up Hermes. They define the semantic enrichment rules, connect Hermes to the LLM provider’s prompt API, and test the rewritten prompts in a staging environment. Ongoing maintenance includes updating the rules as brand terminology evolves.
Does Hermes still improve brand asset surfacing with the latest LLM updates?
Yes, Hermes continues to work as long as the prompt format remains compatible with the new model. You may need to tweak the enrichment schema if the LLM introduces breaking changes, but the core benefit of contextual rewriting stays the same.
What happens if Hermes rewrites a query incorrectly and how can we detect it?
If Hermes misrewrites a query, the generated response may miss your brand assets or surface irrelevant content. You can notice this by reviewing the prompt logs your LLM provider exposes and by tracking drops in click‑through or relevance metrics. Correcting the enrichment rule will fix the issue.
How long does it take for Hermes’s effects to appear in our search analytics after activation?
You’ll start seeing measurable changes within a few days as the enriched prompts begin influencing the model’s outputs. In the meantime, monitor prompt‑log volume and early click‑through rates to confirm that rewriting is occurring. Full impact may take a week or two as traffic patterns stabilize.
Asked out loud
spoken, not typedThe same term in the words somebody uses speaking to an assistant rather than typing into a box — written from the situation, which is why each one carries the situation it came from.
Usually it’s because Hermes hasn’t enriched the query with your brand’s context yet; check the prompt logs to see if the query was rewritten. If the rewrite is missing, you may need to adjust the enrichment rules or ensure the query contains a brand‑related term.
It depends on whether the query contains the product name that Hermes is configured to recognize; if not, the rewrite won’t happen. Verify the query wording and look at the LLM prompt logs for clues, then tweak the query or the enrichment rule accordingly.
Usually you can confirm by examining the prompt logs to see the exact enriched prompt that was sent to the model. If the brand tagline appears in the rewritten prompt, the response should include the correct wording; otherwise, adjust the semantic rules.