✦ Feason

Architecture comparison · Christian AI

Feason Context vs.
a generic theology RAG pipeline

Compare Feason Context with a generic retrieval-augmented generation pipeline for Christian AI: corpus work, provenance, tradition coverage, citation verification, and operational responsibility.

Maintained by Feason

Short answer

Choose for the evidence contract you need.

Use Feason Context when you want a maintained, provider-neutral Christian evidence layer with stable passage identifiers, provenance, explicit tradition coverage, and citation verification. Build a custom pipeline when owning the corpus and retrieval method is itself a core product requirement.

Comparison

Different tools solve different layers.

This table describes product categories and tradeoffs. It does not claim that one approach is universally best.
ApproachBest forProvidesYour responsibility
Feason ContextTeams that need Christian evidence through REST or MCP without operating the corpus.Versioned passages, provenance, tradition attribution, coverage boundaries, evidence packets, and exact citation verification.Model behavior, user experience, final conclusions, pastoral safeguards, and application-specific evaluation.
Managed generic RAGTeams with their own documents that want hosted ingestion, embeddings, retrieval, and scaling.General document processing and retrieval infrastructure, often with configurable chunking and ranking.Source rights, theological taxonomy, edition integrity, disputed-view handling, citation semantics, and domain evaluation.
Custom RAG pipelineResearch or product teams that require complete control of corpus, ranking, hosting, and release policy.Maximum architectural control and the ability to encode a proprietary method.The full ingestion, editorial, retrieval, security, evaluation, governance, and operations lifecycle.

Evaluation criteria

Inspect the evidence, not the pitch.

A useful evaluation can be reproduced from the returned data, documented boundaries, and a small set of representative queries.

Passage identity

Check whether every result has a stable identifier and can be retrieved again after the index changes.

Edition and provenance

Require source title, author, edition or translation, license, canonical reference, and a resolvable provenance path.

Tradition boundaries

Test whether a system distinguishes attributed positions, shared claims, disagreement, and missing corpus coverage.

Negative evidence

A failed retrieval must not silently become proof that a source or tradition has no teaching on the subject.

Citation verification

Ask whether an exact quotation can be compared with an immutable source snapshot rather than trusted from generated prose.

Release evaluation

Inspect retrieval recall, citation integrity, latency, failure behavior, and the process used to approve corpus changes.

Implementation

A bounded way to test the choice.

Start with a narrow corpus question and preserve every boundary the source system reports.
  1. Choose representative questions

    Use questions that require Scripture, a primary historical source, a disputed tradition claim, and an explicitly missing source.

  2. Compare returned evidence

    Score passage relevance, provenance completeness, tradition attribution, caveats, and whether the same citation resolves twice.

  3. Test failure behavior

    Remove a requested tradition or ask for an unsupported quotation. Prefer systems that disclose the gap instead of filling it from model memory.

  4. Measure operational ownership

    Estimate who maintains licenses, snapshots, embeddings, evaluation sets, access control, incident response, and release review.

Questions

Frequently asked.

These answers state Feason’s product boundaries and avoid treating retrieval as a theological verdict.
Is Feason Context a complete AI answer service?

No. Feason returns source-grounded evidence, citations, coverage, and caveats. The calling application chooses its model, prompt, interface, pastoral boundaries, and final wording.

Can a team build the same capabilities with generic RAG?

A team can build a theology retrieval system with general infrastructure. It must still acquire and license sources, preserve editions and provenance, model traditions and disputed claims, evaluate retrieval, verify citations, and operate the index.

When is a custom RAG pipeline the better choice?

A custom pipeline is appropriate when a team needs full control of a proprietary corpus, ranking logic, hosting boundary, or research method and can sustain the editorial and operational work.

Does Feason decide which Christian tradition is correct?

No. Feason attributes disputed positions to sources and traditions, reports missing coverage, and returns evidence rather than a platform-wide theological verdict.

Feason Context · Source-grounded Christian evidence

Run the same question against inspectable sources.