GROQ Does Not Predict Tokens

GROQ Does Not Predict Tokens | Generated by AI

Home 2025.10

Short answer

GROQ itself never “predicts the next token”.
It is a database‑style query language that selects, filters, and reshapes the structured JSON documents you have stored in Sanity.
The “semantic” power you see in Sanity‑based products comes from two separate things:

What you’re asking about	How it is actually done
Understanding the meaning of the content you stored (e.g., “show me all posts about React hooks”)	GROQ uses indexes, column‑store scans and Boolean predicates to find the right documents. The “meaning” is encoded in the explicit fields you have (tags, categories, references, author, etc.) – no neural net needed.
Generating new text or completing a sentence (“write a summary of this article”)	You have to call a large language model (LLM) – OpenAI, Anthropic, Cohere, a self‑hosted transformer, etc. The LLM does the heavy‑weight tensor‑matrix math (K‑Q‑V attention, softmax, etc.). The result is then written back to Sanity (or used as a prompt) via a normal API call.

So the short answer is: Sanity’s speed comes from a classic, highly optimised query engine; any “next‑token prediction” or deep semantic understanding is performed **outside of GROQ, by an external transformer‑based service.**

Below is a more detailed walk‑through that explains why this separation exists, how the two pieces fit together, and what you can do if you need both fast content retrieval and high‑quality language‑model predictions.

1. What GROQ actually does

Feature	What it looks like to the developer	How it works under the hood (no tensors)
Filtering (`*[_type == "post" && publishedAt < now()]`)	“Give me every post that is already published.”	Scans the `_type` and `publishedAt` columns of the Content Lake. Uses a B‑tree/bitmap index to jump directly to matching rows.
Projection (`{title, slug, author->{name}}`)	“Only return these fields, and resolve the author reference.”	Reads only the `title`, `slug`, and `author._ref` columns, then does a join by looking up the referenced author document (again column‑wise).
Ordering & slicing (`\|order(publishedAt desc)[0...10]`)	“Give me the 10 newest posts.”	Uses the sorted `publishedAt` column to produce a pre‑ordered stream; stops after 10 items (no need to materialise the rest).
Full‑text match (`title match "react*"`)	“Find titles that start with ‘react’.”	Leverages a text index (inverted index) that lives alongside the column store, similar to how Elasticsearch works, but built directly into the lake.
Streaming	Results start arriving after the first few rows are ready.	The engine pipelines: source → filter → map → serializer → HTTP response, sending bytes as soon as they’re produced.

All of those operations are deterministic, integer‑based, and I/O‑bounded – they never require matrix multiplication or gradient calculations. That’s why a pure GROQ query typically finishes in single‑digit to low‑double‑digit milliseconds.

2. Where “semantic” and “next‑token” capability does come from

Use‑case	Where the LLM lives	Typical flow (sanity‑centric)
Summarisation	`POST https://api.openai.com/v1/chat/completions` (or any other LLM endpoint)	1️⃣ Use GROQ to fetch the article body. 2️⃣ Send that text as a prompt to the LLM. 3️⃣ Receive the generated summary and write it back (`PATCH /documents/{id}`) via the Sanity API.
Semantic search	Vector‑DB (Pinecone, Weaviate, Qdrant) + embeddings model (OpenAI `text‑embedding‑ada‑002`, etc.)	1️⃣ Export candidate docs → embed once (offline). 2️⃣ Store embeddings in a vector DB. 3️⃣ At query time: embed the user query → nearest‑neighbour search → get list of `_id`s → GROQ `*[_id in $ids]{title,slug}` for the final payload.
Auto‑tagging / classification	Small classifier model (could be a fine‑tuned transformer or even a logistic‑regression on top of embeddings)	1️⃣ Webhook fires on document creation. 2️⃣ Serverless function calls the classifier → receives tags. 3️⃣ Function patches the document with the tags (fast GROQ mutation).
Chat‑assistant that references your content	LLM for dialogue + GROQ to fetch context	1️⃣ User asks “What did we say about caching?” 2️⃣ Backend runs a GROQ query that pulls all relevant sections. 3️⃣ Those sections are inserted into the prompt sent to the LLM. 4️⃣ LLM returns a response; the response can be displayed or stored.

Key point: The semantic heavy lifting (embeddings, attention, token prediction) is performed by the LLM service, not by GROQ. GROQ’s job in those pipelines is simply to fetch the right pieces of structured data (or to store the output).

3. Why the separation makes sense (performance & architecture)

Reason	Explanation
Specialisation	A content store is optimized for exact matches, range scans, and fast projection. A transformer is optimized for probabilistic language modelling. Trying to make one system do both well leads to compromises.
Cost control	Running a transformer on every request would be expensive (GPU minutes). By keeping GROQ cheap, you only pay for the AI part when you actually need it.
Scalability	GROQ queries can be cached at CDN edges, sharded across many workers, and served with sub‑30 ms latency. LLM inference can be off‑loaded to a separate autoscaling pool that may be region‑specific.
Flexibility	You can swap the LLM vendor (OpenAI → Anthropic → locally hosted) without changing any GROQ code. The query language stays stable.
Security & compliance	Structured content stays in your own Sanity project; only the derived embeddings or prompts leave the system, making it easier to audit data flows.

4. If you do want token‑level prediction inside Sanity, how would it look?

Create a serverless function (e.g., Vercel, Cloudflare Workers, AWS Lambda) that receives a request like POST /api/generate.

Inside that function:

// 1️⃣ Pull the context you need
const ctx = await sanityClient.fetch(`*[_id == $docId]{title, body}[0]`, {docId});

// 2️⃣ Build a prompt (maybe with few‑shot examples)
const prompt = `Summarize this article:\n\n${ctx.body}\n\nSummary:`;

// 3️⃣ Call the LLM (OpenAI example)
const resp = await fetch('https://api.openai.com/v1/chat/completions', {
  method: 'POST',
  headers: { 'Authorization': `Bearer ${process.env.OPENAI_KEY}`, 'Content-Type': 'application/json' },
  body: JSON.stringify({
    model: 'gpt-4o-mini',
    messages: [{role: 'user', content: prompt}],
    temperature: 0.2
  })
});
const {content} = (await resp.json()).choices[0].message;

// 4️⃣ Store the result back in Sanity (fast GROQ mutation)
await sanityClient.patch(docId).set({summary: content}).commit();

The latency you see in the client is the sum of:
- Network round‑trip to the LLM (often 30‑120 ms if the LLM is on a fast edge location).
- LLM inference time (depends on model size; small models < 50 ms, large GPT‑4‑class > 300 ms).
- The GROQ mutation (≈ 5‑10 ms).

If you need sub‑100 ms total latency for a chat UI, you typically run the model at the edge (e.g., Cloudflare Workers‑AI, Lambda@Edge, or a locally hosted 8‑bit model) so that the network hop is essentially zero. The GROQ part remains negligible.

5. How to get good semantics without paying for a full‑blown transformer on every request

Technique	What it buys you	Approximate cost (as of 2025)
Pre‑computed embeddings + vector DB	Fast “semantic similarity” look‑ups (< 5 ms per query).	One‑time embedding cost (≈ $0.0001 per 1 k tokens with OpenAI), plus cheap vector‑DB storage.
Fine‑tuned small model (e.g., `distilbert-base-uncased` quantised to int8)	Good classification / tagging on‑device, < 10 ms inference on a modern CPU.	Free if you host it yourself; otherwise $0.02‑$0.05 per 1 k tokens on managed services.
Prompt‑engineering + few‑shot examples	Improves the accuracy of a large LLM without extra fine‑tuning.	Same per‑token cost as the LLM, but you can keep prompts short (≤ 200 tokens) to stay cheap.
Hybrid retrieval‑augmented generation (RAG)	Combine exact keyword match (GROQ) with a small LLM for the final answer.	You only call the LLM on a few retrieved documents, reducing token count dramatically.

Best practice:

Use GROQ first to narrow the candidate set (e.g., fetch all posts of a certain type, or all docs that contain a specific tag).
If you need a “semantic” ranking, run those candidates through a vector similarity step (still cheap).
Only then call the LLM for generation or summarisation on the top‑N items.

6. Frequently asked “What about next‑token prediction?”

Question	Answer
“Can GROQ autocomplete a field as I type?”	No. GROQ returns data; it does not generate new tokens. You would need a separate autocomplete service (e.g., OpenAI `gpt‑3.5‑turbo` with `stream:true`) that you call from your UI, then write the chosen value back to Sanity with a mutation.
“Do Sanity’s built‑in UI components use AI to suggest content?”	The default Studio editor does not use an LLM. Some community plugins (e.g., `sanity-plugin-asset-source-llm`) call external models to generate alt‑text or titles, but that is optional and external.
“If I store the output of a language model in Sanity, will GROQ understand it better?”	The model’s output is just more structured text. GROQ can filter on it (e.g., `summary match "react"`), but any semantic understanding still comes from how you structured the data (tags, categories, embeddings) and not from GROQ itself.
“Can I train a model inside Sanity?”	No. Sanity is a CMS, not a training platform. You can store training data (datasets, annotation JSON) and trigger external training jobs via webhooks, but the model runs elsewhere.

7. TL;DR Checklist for a “fast, semantically aware” product

✅ Goal	How to achieve it
Instant content lookup	Use pure GROQ queries with proper indexes (`_type`, `publishedAt`, custom fields).
Semantic similarity / search	Pre‑compute embeddings → store in a vector DB → at query time: embed the user query → vector‑search → feed resulting IDs into a GROQ filter.
On‑the‑fly text generation (summaries, copy‑writing, chat)	Call an external LLM (OpenAI, Anthropic, self‑hosted) after you have fetched the needed context with GROQ. Write the result back via a normal mutation.
Low latency for the AI part	• Quantise the model or use a small hosted model. • Deploy the inference at the edge (Cloudflare Workers‑AI, Vercel Edge Functions). • Cache recent embeddings or LLM responses when possible.
Keep costs predictable	• Separate the two layers (GROQ = cheap, token‑based LLM = pay‑as‑you‑go). • Batch embeddings offline (once per document). • Use `limit`/`slice` in GROQ to avoid pulling massive payloads into the LLM.

Bottom line

GROQ ≠ transformer. It is a deterministic query engine that can retrieve data in milliseconds because it works on a column‑store with indexes and streaming execution.
Accurate semantics or next‑token generation are outside the scope of GROQ; they come from separate language‑model services that you call when you need them.
By combining the two (fast GROQ for data plumbing + a modern LLM for generative or similarity tasks) you get a system that is both high‑performance and semantically powerful.

If you have a concrete workflow you’d like to optimise—e.g., “autocomplete product descriptions while the editor types” or “search my knowledge base with natural‑language queries”—let me know and I can sketch a concrete architecture diagram and sample code. Happy building!

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