Source Grounded AI Recall

A grounded response should identify the source record, relevant version or date, relationship between the quoted evidence and the claim, and any transformation that produced a summary. It should also state whether the source is current and authoritative for the present purpose. A citation that points to a related document but does not support the sentence is decoration, not grounding.

The response should separate direct observation from interpretation. A product page can establish what was publicly described when captured. It may not establish actual availability for every account, reliability, customer outcomes, or the intent behind the language. A company record can show that a decision was approved, but later action and results require separate evidence. Grounded recall keeps those boundaries visible.

Grounding should also be proportional to the claim. A low-risk orientation answer may cite an approved guide and clearly state its date. A customer commitment or public assertion may require a current domain record, coverage across relevant sources, and review by the role that owns the claim. The architecture should not use one generic confidence label for both situations. It should make the support threshold and review path explicit before the answer can influence higher-consequence work.

Sources can be wrong, incomplete, disputed, or unsuitable for the question. Repetition across copied documents does not create independent support. A recent informal note may conflict with an approved policy. An authoritative source for one domain may have no authority in another. The retrieval system can rank and label these conditions, but the accountable owner may still need to determine which record governs the decision.

Specialist judgment remains necessary when context involves law, finance, security, privacy, medicine, contracts, or another consequential domain. Recall can help an authorized reviewer locate material and understand its lineage. It should not convert retrieved text into professional advice or permission. The useful outcome is a better evidence set for judgment, including a clear indication when the available record is insufficient.

A workflow can distinguish source facts, calculations, summaries, inferences, recommendations, and unknowns. Source facts should point to direct evidence. Calculations should expose inputs and method. Summaries should retain their source coverage. Inferences should be labeled and supported by the observations from which they were drawn. Recommendations should identify the decision owner and should not be restated later as facts.

This classification can occur during context preparation. If a user asks why a project changed direction, the evidence set might include the original goal, customer observations, technical constraint, alternatives considered, decision record, and later outcome. The answer can then state what was observed, what the team concluded, and what it decided. If the decision record is absent, the response should not infer a formal approval from subsequent activity.

Company information often passes through extraction, normalization, summarization, and aggregation before retrieval. Each step can introduce error. Transformation lineage records which source fields or passages contributed, which tool or process transformed them, when the transformation occurred, and whether a person reviewed the result. A derived record should never make the original evidence impossible to locate.

For example, an agent may summarize twenty support conversations into five recurring themes. The theme record should preserve the covered cases, time period, exclusions, and method. It should not imply that all customers share those themes or that frequency proves priority. A later product workflow can use the synthesis as evidence with appropriate limits while still allowing a reviewer to inspect the underlying authorized records.

The system should identify what kind of answer the question requires. A definition may need an approved policy or glossary. A status question may need current operational state. A historical question may need dated records and decisions. A causal question needs evidence that connects events rather than two nearby timestamps. Query planning can route each need to lexical search, structured data, graph relationships, or semantic retrieval.

A hypothetical operations question asks whether a supplier delay caused a customer escalation. Search may find both records, but co-occurrence does not establish causation. Grounded recall can present the shipment event, communication timeline, and escalation record, then state whether an explicit link exists. If the evidence only suggests a relationship, the answer should describe it as an inference and route a material decision to the responsible owner.

After retrieval, the context assembler should remove duplicates, resolve versions, enforce access, and select enough evidence to answer without overwhelming the model. It can include passages, structured facts, record types, dates, and caveats. Conflicting current sources should remain visible. If coverage is incomplete, the package can narrow the requested answer or identify the missing authority.

The evidence set should travel with the response or material decision so a later reviewer can inspect what was available at the time. A new run may refresh the evidence, but it should not rewrite the historical package. This distinction matters when a source changes after an action. The company can correct current guidance while still explaining why an earlier decision relied on a previous valid version.

Two departments may use different definitions, or a new policy may not yet be reflected in every dependent guide. A source-grounded response should identify the records, dates, owners, and applicable scopes. Authority rules may resolve the conflict for a specific workflow. When they do not, the system should mark the issue as unresolved and route it to the person responsible for setting current direction.

Models are skilled at producing a coherent synthesis, which can become a liability when the organization itself has not decided. A blended answer may sound reasonable while creating policy that no owner approved. The context layer should instruct the model to preserve material conflict, and the interface should make dispute visible. Resolution can then create a decision record that future recall can apply within its defined scope.

An abstention should explain what is missing and what would unblock the answer. It might state that the authoritative record is unavailable, that sources are stale, that the requester lacks permission, or that the evidence supports only a narrower conclusion. This is more useful than a generic refusal because it directs the workflow toward source refresh, access review, specialist judgment, or a revised question.

Abstention thresholds should reflect consequence. An internal brainstorming task may proceed with labeled uncertainty, while a customer commitment, public claim, financial action, legal interpretation, or security change may require current direct support. The policy should be defined by accountable owners rather than inferred from model confidence. Fluency and probability scores do not determine business authority.

For each test question, define the claims that are supported, the sources that provide support, records that must be excluded, and the correct limitation when evidence is absent. Include a highly similar but superseded document, a summary derived from the same source, an inaccessible record, an explicit contradiction, and a question whose premise is false. Reviewers should check whether every material sentence stays within the evidence.

Counterfactual tests are especially revealing. Remove the key source and verify that the claim disappears or weakens. Change a version and confirm that current recall updates while historical queries remain accurate. Revoke permission and ensure that restricted content does not leak through a cached summary. Correct an error and verify propagation through indexes, context packages, and future answers.

Reviewers should also compare two plausible sources with different authority. The expected behavior may be to prefer the approved record, present both for a historical question, or refuse to resolve an active dispute. This tests whether grounding follows organizational meaning rather than citation volume. Record the reason for the expected behavior so the evaluation can change when policy changes instead of hard-coding an unexplained answer.

Technical measures can include retrieval precision, source coverage, citation correctness, unsupported-claim rate, conflict detection, abstention quality, and correction latency. Operational measures may include reviewer acceptance, time to locate authoritative support, repeated research, and decisions delayed by missing evidence. These signals should be compared with a baseline and interpreted within the workflow.

A low unsupported-claim rate does not prove that the system is safe for every action, and a fast answer does not prove that it improves decisions. Review cost, privacy incidents, stale-source use, and user overreliance belong in the same evaluation. Teams should avoid public claims about accuracy or efficiency unless current evidence supports the exact configuration, data, and use case being described.

People should be able to inspect a source, dispute a summary, add missing evidence, and route a correction to the record owner. The system should preserve the original source and the corrected operating posture rather than silently editing history. High-impact decisions need an approver who understands both the evidence and the domain consequence, not a checkbox beside an opaque answer.

A hypothetical product review may retrieve prior research and an approved strategy, then identify that new customer evidence challenges an assumption. The agent can prepare the comparison and propose options. The product owner decides whether the strategy changes. That decision becomes a typed record with its evidence and review condition. Recall helps the company learn without allowing the model to declare a new strategy by itself.

Mnemosyne - MemoryOS is the OmegaOS path for source and lineage records, governed retrieval, and reusable company context. A proportionate implementation supplies source-linked evidence to an authorized workflow through a bounded context package. Forge or another governed work path can preserve the related decision and action evidence, while domain systems and accountable people retain authority.

The starting point should be one decision where unsupported recall causes visible rework or risk. Teams should inventory sources, owners, versions, permissions, expected claims, and abstention conditions, then test corrections and exclusions before live influence. They should verify actual connection and deployment posture for the intended environment. Grounded recall is demonstrated by inspectable support and honest limits, not by a product label alone.