Advanced LLM prompt configuration
To troubleshoot LLM extractions, you can configure the full prompt that Sensible inputs to a large language model (LLM). A full prompt is the combination of user-author LLM prompts and additional back-end prompts.
In detail, when you write a prompt using an LLM-based method, Sensible creates a full prompt using the following:
- a prompt introduction
- "context", made up of chunks excerpted from the document and of page metadata. For more information about chunking configuration, see the Notes section.
- concatenated descriptive prompts you configure in an LLM-based method, such as in the List or Query Group methods.
See the following image for an example of a full prompt that Sensible inputs to an LLM for the Query Group method:
The following table shows parameters that configure parts of the full prompt and that are global, or common to all LLM-based methods:
| key | description | global parameters |
|---|---|---|
| A | Overall description of the chunks. The preceding image shows an example of a user-configured context description overriding the default. | Context Description |
| B | Page metadata for chunks. | Page Hinting |
| C | Chunks excerpted from document, concatenated into "context" | Chunk Count Chunk Size Chunk Overlap Percentage Page Range |
| D | Concatenation of all the descriptive prompts you configured in the method. For example, concatenation of all the column descriptions and the overall table description for the NLP Table method. | Description fields |
You can configure the preceding parameters in the JSON editor and in the visual editor.
For example, the following screenshot shows prompt settings in the visual editor for all fields in a config:
To edit the preceding parameters in the visual editor for individual fields, click the field to edit it, then click the advanced settings drop-down. For example, for a List field:
Global LLM-based parameters
The following table shows global parameters, or parameters that are common to more than one LLM-based method. You can configure these parameters for all fields in a config, or on a field-by-field basis.
For parameters specific to an LLM-based method, see its reference topic, for example, the Query Group method reference topic. Like global parameters, method-specific parameters can be set for all fields in a config, or on a field-by-field basis.
| SenseML parameter1 | value | notes | interactions |
|---|---|---|---|
| CONFIGURE CONTEXT SIZE | |||
| chunkCount | number. defaults: Query Group: 5 List: 20 NLP Table: 5 | The number of top-scoring chunks Sensible combines as context as part of the full prompt it submits to an LLM. Often, chunk count and chunk size are related. For example, if you know that your target data are spread over 7-10 pages, and occupy a small portion of those pages, you can specify a chunk count of 10 and a half-page chunk size. | For the List method, the LLM Engine parameter interacts with this parameter. For more information, see the List method. |
| chunkSize | 0.5, 1 defaults:Query Group: 0.5 List: 1 NLP Table: 0.5 | The size of the chunks Sensible splits the document into, as a page fraction. For example, 0.5 specifies each chunk is half a page. Often, chunk count and chunk size are related. For example, if you know that your target data is contained in one cover page, you can set chunk size to 1 and a chunk count to 1. | If you set the Search By Summarization parameter to true, then Sensible sets this parameter to 1 and ignores any configured value. |
| chunkOverlapPercentage | 0, 0.25, 0.5 defaults:Query Group: 0.5 List: 0 NLP Table: 0.5 | The extent to which chunks overlap, as a percentage of the chunks' height. For example, 0.5 specifies each chunk overlaps by half its height. Sensible recommends setting a non-zero overlap to avoid splitting data across chunks. Set overlap to 0 solely if you're confident that your document layout doesn't flow across page boundaries and you're using a one-page chunk size. | If you set the Search By Summarization parameter to true, then Sensible sets this parameter to 0 and ignores any configured value. |
| FIND CONTEXT | |||
| searchBySummarization | boolean. default: false | Set this to true to troubleshoot situations in which Sensible misidentifies the part of the document that contains the answers to your prompts. This parameter is compatible with documents up to 1,280 pages long. When true, Sensible uses a completion-only retrieval-augmented generation (RAG) strategy: Sensible prompts an LLM to summarize each page in the document, prompts a second LLM to return the pages most relevant to your prompt based on the summaries, and extracts the answers to your prompts from those pages. | If you set this parameter to true, then Sensible sets the following for chunk-related parameters and ignores any configured values: - Chunk Size parameter: 1 - Chunk Overlap Percentage parameter: 0 - Chunk Count parameter: 5 - (for the Query Group method) Chunk Scoring Text parameter |
| pageHinting | boolean. default: true | Includes or or removes page metadata for each chunk from the full prompt Sensible inputs to an LLM. If set to true, then you can add location information to a prompt to narrow down the context's location. For example: Location relative to page number and position on page - "address in the top left of the first page of the document" - "What is the medical paid value on the last claim of the second page?" Location relative to content in document - "total amount in the expense table" - "phone number after section 2" Set this to false if page numbers don't add useful information. For example, if your PDF converter automatically applied page numbers to scanned ID cards, set this parameter to false to ignore the page numbers, since their relationship to the cards' text is arbitrary. | |
| pageRange | object | Configures the possible page range for finding the context in the document. If specified, Sensible creates chunks in the page range and ignores other pages. For example, use this parameter to improve performance, or to avoid extracting unwanted data if your prompt has multiple candidate answers. Contains the following parameters: startPage: Zero-based index of the page at which Sensible starts creating chunks (inclusive). endPage: Zero-based index of the page at which Sensible stops creating chunks (exclusive). | Sensible ignores this parameter when searching for a field's anchor. If you want to exclude the field's anchor using a page range, use the Page Range preprocessor instead. |
| MISCELLANEOUS | |||
| contextDescription | string. default: The below context is an excerpt from a document. | Configures context's metadata by overwriting the default context description. For example: - The below context is an excerpt from a scanned index card that contains botanical information about a single plant species, including phenology information.- The below context is an excerpt from an email. Assume the sender is always an automated system from an insurance broker. | |
| confidenceSignals | boolean. defaults: true in visual editor, false in JSON editor | If true, Sensible prompts the LLM to report any uncertainties it has about the accuracy of its response. For more information, see Qualifying LLM accuracy. Sensible currently supports this parameter for the Query Group method. | Not supported if you set the Multimodal Engine parameter on the Query Group method. |
| (Deprecated) promptIntroduction | string | Deprecated. Overwrites the introductory text at the beginning of the full prompt that Sensible submits to the LLM. |
1 Configure these parameters for all fields in a config using the NLP preprocessor or in the visual editor's Configuration settings. You can override a config-level parameter at the field level on an individual basis.
Notes
For an overview of how LLM-based methods work, see the following steps.
- To meet the LLM's input token limit, Sensible splits the document into chunks.
- Sensible selects the most relevant chunks and combines them with page-hinting data to create a "context".
- Sensible creates a full prompt for the LLM that includes the context and the descriptive prompts you configure in the method. For an example of a full prompt, see the beginning of this topic.
- Sensible returns the LLM's response.
For specifics about how each LLM-based method works, see the Notes section for each method's SenseML reference topic, for example, List method.
Updated about 4 hours ago