Conversational and generative AI: differences in customer service

Generative AI: generated replies and verified information

A generative model produces a reply from the supplied context. It can reformulate questions and explain information but does not automatically possess current company rules or an order's status. Select approved sources for rules and use authorized lookups for changing individual data. Assess what the source supports as well as conversational fluency.

Current company rules require available sources. Individual states such as order progress need responsible-system lookups.

Conversation organizes interaction

Service receives requests, clarifies details, maintains subjects and offers next steps. Speech recognition and synthesis participate in voice experiences.

Readable replies may be long aloud. Conversational design includes short questions, data confirmation and channel-appropriate pacing.

Tools connect operations

Configured Tigy tools support in-conversation lookups and actions. Definitions, attachment and instructions guide their use.

Promising request creation does not create a request. Destination responses must prove actions before confirmation.

Evaluate the required process

Test understanding, answer sources, parameters and external outcomes. Include missing information and unavailable systems alongside successful examples.

Labels describe functions; scope determines implementation. Start with tasks and verification rather than assuming capability from names.

Plausible versus verified responses

For fictional delivery questions, reliable answers use actual estimates or acknowledge absence rather than produce plausible dates.

General rules can use approved documents; individual requests may need authorized lookup.

Test missing answers, corrections and exceptions. Creative wording does not authorize invented business facts.

Connect flexible language with a defined task

Generative AI produces responses according to questions and context rather than relying only on fixed phrases. Flexibility can help people describing needs differently. It does not independently establish truthful information or permitted operations. The model's ability to produce convincing wording should therefore be evaluated separately from the system's ability to support a claim.

In a fictional example, one person asks how a purchase is progressing while another asks whether an order has been prepared. Agents can recognize shared intent. Answering about individual records still requires appropriate identification, configured tools and authorized results. Understanding the question is one stage of service rather than proof that access or execution succeeded.

Write expected task outcomes. Reporting policies, querying states and modifying records have distinct requirements. Calling all of them “intelligent service” makes it difficult to determine whether agents accomplished what callers needed. Define observable evidence for each outcome before reviewing dialogue quality.

Begin with a scope whose sources and results are verifiable. Conversations can remain flexible in wording while precise about limits. This combination offers more usefulness than accepting every topic without a way to confirm answers. Include excluded subjects and truthful alternatives so the agent can remain helpful when the request falls outside its available capabilities.

Understand the difference between response and execution

Voice experiences combine speech recognition, interpretation, response generation and audio synthesis. Actions also involve tools and external systems. Problems can emerge at any stage even when final conversations sound natural. Assess the complete chain when investigating outcomes, rather than attributing every failure to the language model.

Models may formulate queries, but integrations execute them and return information. Statements such as “I checked your order” should depend on those results. Do not use fluency as evidence of system access. A tool described in instructions must also be configured and selected before it becomes an operational capability.

In a fictional case, APIs report applications under review. Agents can explain this state according to policy but must not infer approvals or individual deadlines absent from results. Partial information should remain partial. Empty or refused results require different explanations from successful queries; generic reassuring wording should not erase those differences.

During review, compare audio, text, parameters and external states where available. This helps locate causes. Correct transcription with incorrect parameters requires different diagnosis from incorrectly recognized speech. Include outcomes following corrections and unavailable services so evaluation covers how components interact under less ideal conditions. Keep expected behavior concrete enough that reviewers can distinguish unsupported claims from legitimate explanations of returned information.

Give agents sources with context and ownership

Models may know general terminology without knowing a company's current rules. Use approved material for policies, services and procedures. Identify branches, validity and exceptions where they change answers. General familiarity with an industry should not substitute for the actual conditions governing a particular organization.

In Tigy, documents must finish processing and be selected for the agent consulting them. Workspace upload does not automatically associate files with every agent. Verify configuration before investigating unsupported answers. Test both a question covered by the selected source and a question beyond it to check use and limit recognition.

Do not treat shared files as indiscriminate substitutes for individual queries. Service policies may belong in documents; current purchase states should come from authorized systems where integration exists. Keep this distinction clear in instructions so the agent does not present general knowledge as evidence about a specific account.

Assign change owners. Outdated sources can produce coherent incorrect answers. Maintenance should verify active files, agent configuration and affected questions. Editing originals is insufficient when service continues using another version. Record which sources supported the tested configuration, and repeat representative cases after changes. Where approved material conflicts or fails to answer, preserve uncertainty and use the available clarification or referral process rather than synthesizing a new policy.

Write rules guiding observable decisions

Useful instructions explain roles, goals, necessary questions, tools and limits. “Be excellent” does not define what happens when codes are missing. Specific rules can require references, confirmation where uncertain and queries only afterward. Reviewers should be able to observe whether those decisions were followed.

Avoid absolutes conflicting with service. “Never ask questions” prevents clarification; “always resolve everything” encourages promises beyond capabilities. Rules should recognize requests requiring staff or unavailable integrations. Define truthful alternatives that fit the operating process instead of demanding confidence where evidence is missing.

In a fictional example, agents explain cancellation policy but lack cancellation tools. Instructions should preserve that distinction. Agreement with caller intent does not authorize announcing record changes. If request collection is supported, describe its pending state and actual owner without suggesting that collection equals cancellation.

Use examples where they clarify difficult decisions without accumulating redundant variations. Review the full set when scope changes. Instructions should follow effective capabilities so language and operation remain coherent. Check tool names and selected resources against configuration rather than assuming text mentions activate them. Include success and refusal cases in review because rules can appear sensible while producing unsupported action claims under pressure from repeated caller requests.

Test flexibility with correction, ambiguity and limits

Vary wording for the same task and include situations changing decisions. Simple rephrasing evaluates language; corrected references evaluate current-request use; questions outside sources evaluate limit recognition. Keep expected outcomes explicit so flexibility does not become an excuse for accepting any plausible answer.

In a fictional case, callers ask about one branch, interrupt and choose another. Responses should follow current choices, including queries where needed. Do not evaluate only verbal acknowledgment; inspect effectively used data. Include delayed earlier results to check that information about the first branch does not confirm the corrected request.

Test someone asking to ignore a rule or claiming special authorization. That claim does not change company policy. The agent should follow approved verification, and the system must check who may access each record and which changes are allowed. Ask the integration owner to test this protection using fictional data; conversational instructions do not replace system authorization.

Define criteria by task and severity. Long responses and queries to wrong customers require different treatment. Evaluation should capture correct information, permitted operations, outcomes and clarity rather than reducing everything to preference for attractive voices. Preserve critical failures individually even when aggregate performance looks strong. Retest representative cases after instruction, source or integration changes so improvements remain connected to observed behavior rather than a general impression from one successful demonstration.

Expand according to operational ability to sustain outcomes

Initial pilots need owners, sources and exits for unfinished requests. Agent availability does not establish availability of every API or staff team. Explain hours and next steps according to real processes. Avoid promising immediate service or callbacks where the organization has not implemented those commitments.

Review completed, referred and pending conversations. Compare what callers heard with observed outcomes. Ended calls may represent abandonment; transfers may still require later resolution. Use definitions preserving those differences. Separate attempted actions from confirmed results so operational reports do not overstate what automation achieved.

Classify problems by content, instructions, recognition, tools and continuity. This helps choose appropriate changes. Incorrect telephone routing is not fixed by longer instructions, and outdated sources do not become current through different voices. Record concrete failure evidence and owners instead of assigning every issue to one broad category such as “AI quality.”

Add capabilities after validating existing ones. Generative AI provides flexible interfaces, while reliable service depends on evidence and maintenance. Expansion should demonstrate benefits in specific tasks with clear limits and responsibilities. Retain established tests when introducing new operations so added flexibility does not degrade earlier correct behavior. Evaluate outcomes and caller effort alongside conversational quality, then decide whether broader scope is supported by the operation rather than by the model's ability to discuss more subjects.

Use uncertainty to guide the next step

Not every question needs a definitive answer. Missing branches may require clarification; missing approved policies may require explaining how to verify information. Distinguish these situations in tests. Clarification addresses incomplete context, while verification guidance recognizes unavailable sources. Choosing the appropriate next step preserves usefulness without turning fluent language into unsupported certainty.