> ## Documentation Index > Fetch the complete documentation index at: https://assemblyai.com/docs/llms.txt > Use this file to discover all available pages before exploring further. # Decisions ## Overview The decisions endpoint answers a list of questions about one input. Instead of generated text, you get one typed answer per question, each with a probability. Use it for routing, classification, scoring and other decision points in your application, where you need a predictable answer rather than prose. Send the endpoint an `input`, the fully qualified state you'd like to ask questions about, and a list of `questions`, each of which takes one of the following three forms: | Type | Asks | Request fields | Answer fields | | - | - | - | - | | `predicate` | Is this true? | `instructions` | `probability` | | `choice` | Which of these options is it? | `instructions`, `choices` (at least 2, with unique `value`s) | `choice`, `probabilities`, `confidence` | | `score` | Where does it fall on a scale? | `instructions`, `levels` (2 to 10) | `score`, `probabilities`, `confidence` | ## Models | Model | Provider | Regions | Input | | - | - | - | - | | `gpt-6-luna` | OpenAI | US, global | Text; `instructions` can include images | | `jev-1.13.0` | Typesafe AI (Jev) | US | Text only | Use the exact model ID. Model aliases aren't accepted on this endpoint. > [!NOTE] > > Jev is an opt-in provider and is disabled by default. To use `jev-1.13.0`, review and accept the Jev opt-in terms on the [Data Controls](https://www.assemblyai.com/dashboard/settings/data-controls) page in your dashboard. Until you do, requests to Jev return `400` with a link to that page. ## Make a request This example asks three questions about a support call transcript. ```bash title="cURL" theme={null} curl -X POST "https://llm-gateway.assemblyai.com/v1/decisions" \ -H "Authorization: YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "gpt-6-luna", "input": "Agent: Thanks for calling Northwind Internet, this is Priya. Can I get your account number?\nCustomer: Sure, it'\''s 4471-2209.\nAgent: Thanks. And can you confirm the address on the account?\nCustomer: 18 Harbor Lane. My internet has dropped three times today and I work from home.\nAgent: I'\''m sorry about that. There'\''s an outage in your area. A technician is already on it, and it should be fixed by 5 PM.\nCustomer: Okay, that'\''s fine. Thanks for letting me know.", "questions": [ { "type": "predicate", "name": "confirmed_account_details", "instructions": "Did the agent ask the caller to confirm their account details?" }, { "type": "choice", "name": "call_outcome", "instructions": "How did the call end?", "choices": [ {"value": "resolved", "description": "The issue was fixed or fully answered on the call"}, {"value": "follow_up", "description": "The customer was told the issue will be fixed later"}, {"value": "escalated", "description": "The call was transferred or escalated"} ] }, { "type": "score", "name": "customer_sentiment", "instructions": "How did the customer feel at the end of the call?", "levels": [ {"label": "negative", "description": "Upset or frustrated"}, {"label": "neutral", "description": "Neither upset nor pleased"}, {"label": "positive", "description": "Satisfied or thankful"} ] } ] }' ``` ```python title="Python" theme={null} import requests transcript = ( "Agent: Thanks for calling Northwind Internet, this is Priya. Can I get your account number?\n" "Customer: Sure, it's 4471-2209.\n" "Agent: Thanks. And can you confirm the address on the account?\n" "Customer: 18 Harbor Lane. My internet has dropped three times today and I work from home.\n" "Agent: I'm sorry about that. There's an outage in your area. A technician is already on it, and it should be fixed by 5 PM.\n" "Customer: Okay, that's fine. Thanks for letting me know." ) response = requests.post( "https://llm-gateway.assemblyai.com/v1/decisions", headers={"authorization": ""}, json={ "model": "gpt-6-luna", "input": transcript, "questions": [ { "type": "predicate", "name": "confirmed_account_details", "instructions": "Did the agent ask the caller to confirm their account details?", }, { "type": "choice", "name": "call_outcome", "instructions": "How did the call end?", "choices": [ {"value": "resolved", "description": "The issue was fixed or fully answered on the call"}, {"value": "follow_up", "description": "The customer was told the issue will be fixed later"}, {"value": "escalated", "description": "The call was transferred or escalated"}, ], }, { "type": "score", "name": "customer_sentiment", "instructions": "How did the customer feel at the end of the call?", "levels": [ {"label": "negative", "description": "Upset or frustrated"}, {"label": "neutral", "description": "Neither upset nor pleased"}, {"label": "positive", "description": "Satisfied or thankful"}, ], }, ], }, ) for answer in response.json()["answers"]: print(answer) ``` `input` is the evidence every question is asked about. It can be a string, such as a transcript's text, or an array of user messages made of `input_text` parts. `instructions` can be a string, or an array of `input_text` parts (and `input_image` parts on `gpt-6-luna`). ## Read the response Answers come back in the order the questions were asked. The response also includes `request_id`, `model` and `usage`, which are omitted here. ```json theme={null} { "answers": [ {"type": "predicate", "name": "confirmed_account_details", "probability": 1}, { "type": "choice", "name": "call_outcome", "choice": "follow_up", "probabilities": [ {"value": "resolved", "probability": 0.01}, {"value": "follow_up", "probability": 0.99}, {"value": "escalated", "probability": 0} ], "confidence": 0.99 }, { "type": "score", "name": "customer_sentiment", "score": 1.99, "probabilities": [ {"value": 0, "label": "negative", "probability": 0}, {"value": 1, "label": "neutral", "probability": 0.01}, {"value": 2, "label": "positive", "probability": 0.99} ], "confidence": 0.99 } ] } ``` * **`predicate`:** `probability` is the probability that the statement is true. * **`choice`:** `choice` is the most likely option. `probabilities` lists every option in the order you gave them. * **`score`:** `score` is the expected level index, weighted by probability, so it can fall between levels. For a single level, take the one with the highest probability in `probabilities`. Each entry's `value` is the level's index. ## Fallbacks Decisions supports [fallbacks](https://assemblyai.com/docs/llm-gateway/fallback.md). A fallback can omit `input` and `questions`, which it inherits from the main request. Nested fallbacks aren't supported, and `fallback_config.depth` can be at most `2`. ```json theme={null} { "model": "gpt-6-luna", "input": "...", "questions": [...], "fallbacks": [{"model": "jev-1.13.0"}] } ``` A fallback to Jev requires the Jev opt-in. If the request uses image parts, the Jev fallback can't run them. ## Regions and global routing Decisions is available on the US endpoint (`https://llm-gateway.assemblyai.com/v1`). It isn't available on the EU endpoint. Set `model_region` to `"global"` to use [global routing](https://assemblyai.com/docs/llm-gateway/cloud-endpoints-and-data-residency.md#global-routing) with `gpt-6-luna`. Jev doesn't support global routing. A fallback uses the main request's `model_region` unless it sets its own. ## Prompt caching `gpt-6-luna` caches the `input` automatically, so requests that reuse the same input with different questions read it from the cache. Set `prompt_cache_key` to keep requests that should share a cache together. Cached tokens are reported in `usage.input_tokens_details.cached_tokens`. See [Prompt caching](https://assemblyai.com/docs/llm-gateway/prompt-caching.md). ## Limits and errors * A request can have 1 to 400 questions. Question names must be unique. * Unknown request fields are rejected. * Invalid requests return `400`, with every problem listed in `metadata.errors`. See [Rate limits](https://assemblyai.com/docs/llm-gateway/rate-limits.md) and [Troubleshooting](https://assemblyai.com/docs/llm-gateway/troubleshooting.md) for other status codes. ## API reference See [Create a decision](https://assemblyai.com/docs/llm-gateway/api-reference/create-decision.md) for every request and response field.