Bound the knowledge base through real questions
The AI support plan defines the topics the chatbot can answer. Product descriptions, delivery, membership, internal procedures and personal account lookups require different sources. Collect genuine user questions and map each to an approved answer source. Define an explanation and handoff route for topics outside the scope.
In a RAG approach, the system can retrieve relevant source passages and provide them as context for answer generation. This does not mean that every document upload retrains model weights. Retrieval, generation and model training are separate concepts. Adding sources does not guarantee correct or faithful answers; relevant information must be found and used appropriately.
Start with a bounded question group. Adding an entire archive can increase relevant material while introducing repetition and conflict. The first version should answer the approved scope reliably. Assigning questions to product, support or operations owners makes unresolved information easier to close.
Question scope
Expected customer questions and explicitly excluded topics.
Source scope
The document and approved version supporting the answer.
Action scope
Information versus live record lookup or change authority.
Resolve authoritative versions and contradictions
In an AI agent workflow, the source provides the basis for a decision. Record title, owner, language, product or process scope, version and review state for each document. Creation date alone does not establish currency. Resolve competing guidance from different teams before indexing it.
In an original illustrative scenario, a webpage states seven-day delivery while an older file states thirty days. The technical team should not choose randomly. The business owner approves the current condition and archives the obsolete version. This is not a Prix implementation or customer result. Letting a model decide company policy does not resolve the contradiction.
Classify public information, internal guidance and customer-specific records separately. Do not embed changing stock or order status in a static document and treat it as a certain live answer. An authorized lookup is a separate implementation task. A document inventory does not independently expand the actions a chatbot is permitted to perform.
FROM READING TO A NEXT STEP
Review your knowledge sources
Share the approved documents, user questions and content owners.
Make content readable without losing its conditions
The content preparation process should check meaning before ingestion. Use clear headings, definitions, conditions and exceptions. Add context where a passage only says “the table above.” Relevant product names, units and validity conditions should remain attached to the information used for an answer.
Inspect extracted content from PDFs and images. Columns can become mixed, a table price can attach to the wrong product, or scanned text can be incomplete. Successful upload does not establish successful extraction. The technical team should expose indexed text so an editor can review representative passages.
No single token length fits every document. A short condition and a long procedure need different treatment. Chunking should help retrieval while preserving necessary exceptions. Review Turkish and English content for actual meaning equivalence; automatic translation should not silently change local policy or product conditions.
Apply access rules before generating the answer
A customer messaging chatbot should use only information the person may access. Uploading private material and adding “do not reveal secrets” to a prompt is not an adequate access-control design. Apply relevant identity and document permissions in source selection and retrieval. Personal order data and general delivery guidance require different access decisions.
Do not treat instructions embedded in sources as system authority. A document or customer message should not redefine the chatbot’s role or record permissions. Exclude secret credentials and unnecessary personal information from the knowledge scope. Assign retention, access and deletion responsibilities rather than leaving content ownership to technical assumptions.
Define the expected behavior for absent, conflicting or unauthorized evidence. The chatbot can explain the limitation and hand off where appropriate. Showing a source link does not independently prove correctness; the linked evidence must support the actual condition stated. A clear limitation can be more useful than a persuasive unsupported answer.
Evaluate retrieval and answer quality separately
The automation acceptance plan should observe retrieval and answer quality as distinct stages. Record the question, expected source, necessary conditions and unacceptable response types. A retrieved source with an omitted exception requires a different correction from failure to find the source. Fluency alone is not an acceptance criterion.
Include normal questions alongside short, misspelled, multilingual, excluded and unanswerable questions. Business owners should review expected answers. Automated evaluation may assist, but one model score does not verify every business risk or permission condition. Use human review to explain important failures concretely.
The handover should contain the source inventory, prepared content, permission map, question set and update workflow. When product, price or policy changes, identify the source to revise and the questions to recheck. Verify how obsolete content is removed from indexes and caches. A knowledge base is maintained content with an owner, rather than a completed pile of files.
Verify sources
Resolve owners, versions, permissions and contradictions.
Run question tests
Examine source retrieval and faithful conditional answers separately.
Update and recheck
Mark changed information and reassess affected questions in the new version.
BEFORE YOU DECIDE
Frequently asked questions
Is every PDF suitable for a chatbot?
No. Check extraction, table relationships, currency and permissions. Upload success does not establish content readiness. Map the authoritative version to the user question first.
Does uploading documents train the model?
In a retrieval-based system, documents can provide answer context without automatically changing model weights. Training, indexing and generation are separate processes. The technical team should explain the implemented approach.
What should happen when the answer is absent?
Define the behavior in advance. An explicit limitation, clarifying question or appropriate handoff may be suitable. Do not invent company policy from general model knowledge to fill a missing source.
Can Turkish and English content be used together?
Yes, with review of meaning, versions and equivalent conditions. Language metadata and question tests matter. An obsolete policy in one language should not replace the current guidance in another.
Who should manage updates?
The business owner approves correctness and validity; the technical team verifies ingestion and access behavior. Recheck affected questions after changes. Ownerless content tends to weaken chatbot quality over time.
LET’S DEFINE THE SCOPE
Review your knowledge sources
Share the approved documents, user questions and content owners.
Discuss knowledge preparation