Produce clean, grounded representations to use with agents and RAG by using Content Understanding
AI-103 › Unit 5: Implement information extraction solutions › Extract content from documents › Produce clean, grounded representations to use with agents and RAG by using Content Understanding
Produce clean, grounded representations to use with agents and RAG by using Content Understanding
"Clean" and "grounded" are two separate properties, and Content Understanding is built to deliver both. Clean means output an agent or index can consume without glue code — the documentation says video output "can drop straight into a vector store… no post-processing is required". Grounded means every value carries a confidence score and a pointer to where it came from. Together they are what make bulk extraction trustworthy enough to act on.
Why This Matters
Messy input is the actual problem. Content Understanding "turns messy, multimodal file inputs into predictable, standardized inputs", delivering "clean markdown representations for reasoning and knowledge workflows".
Grounding is a configuration, not a default. estimateFieldSourceAndConfidence is what produces confidence scores and source regions.
Figures become searchable. "Figure descriptions capture information from charts, diagrams, and visualizations, making them searchable" — content that is otherwise invisible to an index.
Prerequisites
- Content Understanding's analyzer,
fieldSchema, and field methods. - That only JSON is indexable.
- Document Intelligence's layout output and paragraph roles.
- Retrieval and Groundedness evaluators.
Learning Objectives
By the end of this lesson you will be able to:
- Explain what makes a representation clean for agents and RAG.
- Enable confidence and grounding, and use them operationally.
- Apply figure description, layout preservation, and annotation detection.
- Choose prebuilt analyzers tuned for RAG.
- Verify representation quality before building on it.
Building Blocks
The agentic framing. Content Understanding "turns messy, multimodal file inputs into predictable, standardized inputs. It delivers clean markdown representations for reasoning and knowledge workflows, ensuring clarity and context for downstream tasks. When structured data is required, it provides schema-aligned key-value fields with confidence scores and grounding, enabling agents to automate decisions with accuracy and auditability."
The RAG framing. It "enables ingestion of content of any modality into a search index, with extensive support for figure description and analysis to make your data more accessible", and "offers multiple prebuilt analyzers that are fine-tuned to give you the best outputs for your RAG search scenarios".
What RAG ingestion recovers. "Figure descriptions capture information from charts, diagrams, and visualizations, making them searchable. Layout analysis preserves document structure including tables, sections, and hierarchies. Annotation detection captures handwritten notes, underlines, and strikeouts."
Confidence and grounding. Confidence scores — "reliability estimates from 0 to 1 for each extracted field value. High scores indicate accurate data extraction, enabling straight-through processing in automation workflows." Grounding — "identifies the specific regions in the content where each value was extracted or generated. Source grounding allows users in automation scenarios to quickly verify the correctness of field values by tracing them back to their origin." Both enabled by estimateFieldSourceAndConfidence.
Output formats. "Content can be output as Markdown for search and retrieval scenarios, or as structured JSON matching your defined schema for automation and analytics workflows."
Prebuilt analyzers for RAG. prebuilt-imageSearch, prebuilt-audioSearch, prebuilt-videoSearch, prebuilt-videoAnalysis — the video output "can drop straight into a vector store to enable an agent or RAG workflow — no post-processing is required".
Contextualization. "The contextualization layer prepares context for generative models and post-processes their output. It includes output normalization and formatting, source grounding calculation, confidence score computation, and context engineering to optimize model usage."
Markdown against structured JSON
| Attribute | ||
|---|---|---|
| For | Search and retrieval | Automation and analytics |
| Consumer | An index, a vector store, a model's context | Code binding to fields |
| Shape | Readable text preserving structure | Your schema |
| Verification | Grounding on generated fields | Confidence per field + grounding |
| Post-processing | None required | None required |
Deep Dive
What "clean" actually means
The word is doing specific work. A clean representation is one a downstream consumer can use without glue code, and three properties make it so.
Predictability. The analyzer "consistently applies these settings to all incoming data", so every file in a corpus produces the same shape. That is what lets an index schema, a prompt template, or a downstream parser be written once.
Structure preserved, not flattened. "Layout analysis preserves document structure including tables, sections, and hierarchies." A representation that flattens a table into prose is smaller and worse — the relationships are gone, and no amount of retrieval recovers them.
Nothing silently dropped. The documentation names three classes of content that ordinary text extraction loses: figures ("charts, diagrams, and visualizations"), structure (tables, sections, hierarchies), and annotations ("handwritten notes, underlines, and strikeouts"). Each is a real class of meaning in real documents — a handwritten margin note or a struck-out clause can invert what a contract says.
The measurable claim is the video one: output "can drop straight into a vector store… no post-processing is required". Where a representation needs a bespoke parser before it is usable, it is not clean, and that parser becomes a maintenance liability tied to the analyzer's version.
Producing a clean, grounded representation
Start with a RAG-tuned prebuilt
Analyzers "fine-tuned to give you the best outputs for your RAG search scenarios".
Grounding, and why it is the harder half
Confidence and grounding together turn extraction from a black box into something auditable.
Confidence scores are "reliability estimates from 0 to 1 for each extracted field value", and their documented purpose is operational: "enabling straight-through processing in automation workflows" by "minimizing the cost of human review". The pattern is routing — process high-confidence values automatically, queue low-confidence ones.
Grounding is the more interesting half. It "identifies the specific regions in the content where each value was extracted or generated", so a reviewer can "quickly verify the correctness of field values by tracing them back to their origin".
That distinction matters. A confidence score tells you how much to trust a value; grounding tells you where to look to check it. A reviewer handed a low-confidence field plus its region can resolve it in seconds; the same field without a region means opening the document and searching.
Both come from estimateFieldSourceAndConfidence, which is a setting rather than a default — so a pipeline built without it produces values with no reliability signal and no trace, and retrofitting means re-analysing the corpus.
The phrase to carry is the documentation's own: agents "automate decisions with accuracy and auditability". Accuracy is the confidence score; auditability is the grounding.
What a plain text extraction loses
Three named classes, each with real consequences.
Figures. "Figure descriptions capture information from charts, diagrams, and visualizations, making them searchable." A quarterly report's key finding often lives in a chart, and a text-only extraction indexes the caption and loses the data. This is content that exists in the document and is simply absent from the index.
Structure. "Layout analysis preserves document structure including tables, sections, and hierarchies." Flattened, a table's rows and columns stop being related, and a section's scope stops constraining what is inside it — so a rule retrieved from one section can be applied to another.
Annotations. "Annotation detection captures handwritten notes, underlines, and strikeouts." This is the one people never anticipate, and it can invert meaning: a struck-out clause still reads as present in plain text, and a handwritten amendment is the operative term in many real documents.
The common thread is that all three failures are silent and invisible from the index side. The document contains the information; the representation does not; retrieval and groundedness both look healthy; the answer is wrong.
Choosing the format, and verifying
Markdown is for "search and retrieval scenarios" — an index, a vector store, or a model's context. It preserves readable structure and needs no parser.
Structured JSON is for "automation and analytics workflows", matching your schema so code binds to named fields, each with its own confidence.
Many pipelines want both: markdown chunks for retrieval, plus a small set of typed fields for filtering and routing. Since one analyzer pass can produce both, that is one reading of the document rather than two.
Verification is the step that separates a trustworthy pipeline from a plausible one, and it has a specific shape. Sample deliberately chosen hard cases — a document with a large table, one with a chart carrying the key finding, one with handwritten annotations, one heavily struck through — and compare the representation against the source. Confirm the table's relationships survived, the chart's content appears, the annotation was captured, and the confidence scores look sane.
Doing that on twenty documents before ingesting a hundred thousand is the highest-leverage hour in the whole pipeline, because after ingestion every fix is a re-ingestion.
Worked Examples
Example 1 — the finding in the chart. Quarterly reports are indexed and questions about figures return the caption but never the data.
Figure description is what recovers this: it "captures information from charts, diagrams, and visualizations, making them searchable". A text-only extraction indexes surrounding prose and the chart's content is simply absent — the document has it and the representation does not, which no retrieval or prompting change fixes.
Example 2 — a struck-out clause treated as active. A contract assistant cites a clause that was struck through in the signed copy.
Annotation detection "captures handwritten notes, underlines, and strikeouts". Without it the struck clause reads as ordinary text, so the answer is perfectly grounded in a representation that lost the deletion. This is why groundedness cannot detect it and why sampling annotated documents belongs in verification.
Example 3 — values with no way to check them. A team wants straight-through processing but reviews every extracted field because they cannot tell which to trust.
Enable estimateFieldSourceAndConfidence for confidence scores 0–1 and grounding to source regions — the documented route to "straight-through processing… minimizing the cost of human review". Route high-confidence values automatically and queue low-confidence ones with their region attached, so a reviewer knows where to look rather than searching the document.
Visual Explanations
What a clean representation preserves:
Why representation quality is invisible downstream:
Common Mistakes
Leaving estimateFieldSourceAndConfidence off and losing confidence and grounding.
Assuming plain text extraction is sufficient. Figures, structure, and annotations are lost.
Flattening tables. Relationships are gone and unrecoverable.
Ignoring strikeouts and handwritten notes. They can invert meaning.
Building a parser around the output. Clean output needs none.
Choosing one output format where markdown and JSON serve different consumers.
Trying to measure representation quality from the answer side. Groundedness scores the representation.
Reviewing everything or nothing. Route on confidence.
Skipping verification and discovering the ceiling after ingesting the corpus.
Practice Exercises
- Define "clean" and "grounded" with the documented phrasing.
- Name the three content classes ordinary text extraction loses.
- Distinguish what a confidence score tells you from what grounding tells you.
- Why can representation quality not be measured from the answer side?
- Which output format for which consumer, and can you have both?
▶Answers
- Clean — "clean markdown representations for reasoning and knowledge workflows", turning "messy, multimodal file inputs into predictable, standardized inputs", with video output that "can drop straight into a vector store… no post-processing is required". Grounded — confidence scores from 0 to 1 plus grounding identifying "the specific regions in the content where each value was extracted or generated", enabling "accuracy and auditability".
- Figures — "charts, diagrams, and visualizations", recovered by figure description. Structure — "tables, sections, and hierarchies", preserved by layout analysis. Annotations — "handwritten notes, underlines, and strikeouts", recovered by annotation detection.
- A confidence score tells you how much to trust the value — a reliability estimate from 0 to 1 enabling straight-through processing. Grounding tells you where to look to check it — the specific region the value came from, so a reviewer can trace it to its origin in seconds.
- Because every downstream evaluator measures the representation, not the document. Groundedness confirms fidelity to the text supplied, which a representation that lost a strikeout or a chart still satisfies — so the answer is faithful and wrong with healthy scores. The only check is sampling representations against source documents, before ingestion.
- Markdown for "search and retrieval scenarios" — an index, vector store, or model context. Structured JSON for "automation and analytics workflows" — code binding to named fields. Yes, both: one analyzer pass can produce markdown chunks for retrieval plus typed fields for filtering, from a single reading.
Summary & Concept Map
Content Understanding exists to turn "messy, multimodal file inputs into predictable, standardized inputs", and the two properties that matter are separable. Clean means output needing no glue code — markdown for search and retrieval or schema-aligned JSON for automation, with structure preserved rather than flattened, and with the three classes ordinary extraction loses recovered: figure descriptions for charts and diagrams, layout analysis for tables, sections, and hierarchies, and annotation detection for handwritten notes, underlines, and strikeouts. Grounded means confidence scores from 0 to 1 telling you how much to trust a value and grounding telling you where to look, both from estimateFieldSourceAndConfidence, together enabling "straight-through processing" with "accuracy and auditability". And because every downstream evaluator scores the representation, quality can only be checked by sampling against the source documents — before the corpus is ingested.
Sources and freshness
Written against current Microsoft Learn documentation for the AI-103 skills measured (16 April 2026), reviewed 2026-08-20. Microsoft Learn controls every changing product contract — availability, preview status, quotas, limits, regional support, naming, and retirement dates all move independently of this lesson. Where a scenario turns on a specific number or a GA/preview boundary, confirm it against the product's own page before relying on it.