/ THE SHORT ANSWER
- 01Test mixed-language questions and local vocabulary explicitly.
- 02Measure retrieval, answer correctness and refusal separately.
- 03Use qualified staff to approve safety-critical source material.
/ dotSuper point of view
Transforms interest in RAG into an evidence-driven industrial deployment decision.
Start from a decision the operator needs to make
Choose a narrower purpose: finding the approved inspection instruction for a part family, locating the correct maintenance reference, or identifying which supervisor owns an unresolved process question.
The IndicTrans2 research provides a primary reference for Indian-language translation resources.
[1] It does not establish that a retrieval system will understand the vocabulary of a particular factory.
Translation, document search and answer generation are separate components, and each can lose the meaning needed for the final decision.
MSME Lean's FAQ gives context on the scheme's manufacturing focus and implementation structure.
[2] The evaluation method here is an independent dotSuper proposal.
It should complement the factory's existing quality and safety processes, with approved instructions and qualified reviewers supplying the actual operational authority.
Build a question set from working language
Include English part names embedded in Hindi or Tamil sentences, transliterated words and abbreviations used on the shop floor.
Do not rewrite every question into polished English before testing.
For each question, record the expected source document, revision and relevant passage.
Add the minimum facts a correct answer must preserve.
If several answers are acceptable, describe the acceptable range and required escalation.
This creates a reviewable task definition instead of relying on whether a response sounds sensible.
Include questions with no approved answer.
A system should be able to say that the available evidence is insufficient and identify the responsible human route.
Otherwise, the evaluation rewards confident completion and overlooks the cases where abstention protects an operator from following an invented or obsolete instruction.
Score retrieval before judging the prose
If it does not, a well-written answer cannot repair the evidence gap.
Record whether the failure came from language handling, part identification, document indexing, access filtering or revision selection.
Then assess the generated answer against the expected facts.
Preserve numbers, units, conditional steps and warnings exactly as the approved process requires.
Avoid treating a citation as sufficient proof: it must point to a passage that actually supports the statement.
A link to the right manual with the wrong revision can still mislead.
Use the table as a proposed evaluation structure.
Reviewers should include both a bilingual user familiar with the local vocabulary and a person qualified to judge the underlying process.
Where one individual fills both roles, make the two judgements explicit so language fluency does not obscure a technical error.
| Dimension | Question for reviewer | Failure to record |
|---|---|---|
| Retrieval | Was the correct approved passage found? | Missing or irrelevant source |
| Revision | Was the applicable version used? | Obsolete instruction |
| Meaning | Were numbers and conditions preserved? | Changed operational meaning |
| Access | Was evidence permitted for this user? | Restricted content exposure |
| Abstention | Did insufficient evidence trigger escalation? | Unsupported confident answer |
Worked example: uncover a hidden retrieval gap
Forty use English wording and forty use mixed Hindi and English.
Suppose the correct current source is retrieved for 36 English questions and 28 mixed-language questions.
Retrieval success is 90% and 70% respectively, with 64 of 80 overall, or 80%.
Now assume the final answer is correct in 60 cases.
A report showing only 75% answer correctness would miss the 20-point language gap in retrieval.
The team should first investigate indexing and query handling for mixed-language questions instead of changing the answer prompt and hoping the evidence improves.
These are illustrative numbers, not a measured model comparison.
Reuse the evaluation structure, not its assumed results.
Add a held-out question set before choosing a production configuration so the team does not tune repeatedly to the same examples and mistake memorisation for reliable performance.
Test revisions, permissions and ambiguity
Ask a question that would produce different answers under the two versions.
The system should retrieve the applicable approved source and expose its revision, rather than blending both instructions into a new procedure.
Test access boundaries with questions about documents outside the user's role.
Apply permission controls before restricted passages enter the model context.
A refusal after the model has already seen confidential material does not provide the same protection.
Include temporary workers and shared terminals in the access design where relevant.
Ambiguous queries need a clarification path.
If two machines share a nickname, ask the user for the asset identifier before presenting an instruction.
Measure whether clarification resolves the query successfully.
Counting every clarification as a failure would encourage the system to guess precisely where a careful question is useful.
Turn the evaluation into a deployment decision
Define which question categories the first release may answer and which it must route to a human.
Keep the authoritative instruction available directly, even when the conversational layer is unavailable.
Track unsupported answers, wrong-revision retrieval, access failures and successful abstentions.
Review the categories separately.
Add fresh examples when operators introduce new terminology or the factory updates its documents, and preserve the evaluation version used for each release decision.
Bring dotSuper a permissioned document set, sample operator questions and the process-owner map.
An AI Readiness Sprint can scope an evaluation harness and a limited search pilot.
The objective is evidence that the system supports a defined task in the languages actually used, with production safety and technical approval remaining with the factory.
What this page cannot conclude
- 01No production system or language model was benchmarked for this article.
- 02Factory procedures, safety controls and acceptable risk require qualified local review.
- 03This article was researched and drafted with AI assistance. Sources and limitations are provided for scrutiny; it is not an independent professional review or a compliance certification.
Sources
- 01IndicTrans2: translation models for Indian languagesAI4Bharat researchers · accessed Sep 15, 2026
- 02MSME Lean frequently asked questionsMinistry of MSME · accessed Sep 15, 2026
This article was researched and drafted with AI assistance. Sources and limitations are provided for scrutiny; it is not an independent professional review or a compliance certification.
Our editorial standard · Found an error? Send a correction with its source.
/ CITE OR SHARE THIS GUIDE
Make the evidence easy to verify.
When you reference this guide, link to its canonical URL. That gives readers one stable place for the evidence, limitations and future updates.
dotSuper Research Desk. (September 15, 2026). Evaluate Indian-Language Factory Search With Real Operator Questions. dotSuper. https://dotsuper.net/feeds/applied-systems/india-indian-language-rag-evaluation