Specialized LLMs and language analysis

Define the decision before choosing the model

Finding a tolerance in a technical manual, classifying a contractual obligation and summarizing a call have different error costs. We first define the user role, data owner, downstream process and consequences of an incorrect answer. Model selection follows this analysis; a classifier or deterministic parser may be sufficient for a bounded task.

The initial output is a task contract describing accepted inputs, expected output structure, citation requirements, latency budget and escalation conditions. It establishes a repeatable evaluation target. A fluent demonstration is useful for exploration, but cannot establish which decisions the system may safely support.

Corpus engineering beyond document chunking

PDFs, tables, email and transcripts need different extraction paths. Each source carries a document identifier, version, page or time range, language and access group. Sampling checks OCR errors, Turkish characters, units and detached table headings. Historical and current procedures remain distinguishable so retrieval cannot silently substitute an obsolete instruction.

Randomly splitting chunks can place near-identical passages from one document in both training and evaluation. Splits should follow meaningful boundaries such as document families, organizations or time periods. Permitted data use, retention and redaction rules belong in dataset preparation, rather than being postponed until a prototype is deployed.

Compare retrieval, fine-tuning and smaller models

For frequently changing knowledge, research examines retrieval with access controls. Fine-tuning for output structure or domain language is a separate experiment and does not replace a current document repository. Smaller models for terminology extraction, entity recognition and routing may offer better cost or explainability for narrowly defined tasks.

Comparisons use the same questions, document versions and authorization context. Changing chunk size, reranking, context length and output templates simultaneously obscures the cause of improvement. Experiment records isolate these variables and report gains, costs and failure categories together, including cases where a simpler baseline performs better.

Answer correctness, grounding and abstention

Retrieval success and final-answer correctness are separate measurements. Even with the correct passage, a model may alter a unit or omit a condition. Evaluation includes answerable, unanswerable, contradictory, outdated and unauthorized questions. A citation is only useful when its source actually supports the accompanying claim, not merely when its URL resolves.

In a hypothetical set of 100 questions, 60 correct answers, five incorrect answers and 35 abstentions yield about 92.3% accuracy among answered questions and 65% coverage. Neither number replaces the other. Critical errors and question slices are reported separately, and a high average score cannot compensate for an authorization failure.

Trust boundaries and operating cost

Retrieved content is treated as data, not as executable system instruction. User and document permissions apply during retrieval; removing words from a generated answer is not access control. In tool-enabled workflows, a model proposal remains separate from permission to execute an operation. Data classification determines where inputs, outputs and logs may be processed.

Operational evaluation covers queues, long documents, concurrent requests and timeout behavior, rather than average latency alone. The same evaluation runs after document or model changes. Reviewed user feedback becomes a regression case; unreviewed conversations are not automatically promoted into training data. Retention and traceability must remain consistent with the agreed data scope.

Was domain knowledge actually learned?

Repeating a technical term does not establish correct use. Evaluation paraphrases a concept and introduces counterexamples with changed units, negation or conditions. Turkish morphology, abbreviations and spelling variants receive dedicated slices. If a tolerance applies only at a particular temperature, omitting that condition is an error. Answer length or keyword presence may miss the semantic failure, so adjudication examines the individual claim and its evidence.

Retrieval evaluation asks whether required evidence appears in the returned set; generation evaluation asks whether the answer uses it correctly. If supplying the correct passage resolves the failure, retrieval may be the bottleneck. If the error persists with sufficient context, investigate the model or task contract. This controlled separation is more informative than responding to every failure with a larger model. Comparisons keep question and document versions fixed.

Language-analysis work defines entity types and terminology around the workflow. Product codes, measurements and personal names are different categories. Reviewer disagreements receive separate examination, and annotation-guide revisions preserve earlier result versions. Schema validity is not semantic correctness: valid JSON can contain an incorrect measurement. Structural validation and meaning therefore require separate acceptance checks rather than one combined pass indicator.

Research outputs and acceptance evidence

A project can define a task specification, data inventory, baseline method, comparative experiment report, error taxonomy and acceptance criteria for a controlled pilot. Research evidence does not establish that every product feature is production ready. Organization-specific integration, infrastructure and maintenance responsibilities require a separate technical assessment.

Gokbilge Tech approaches this work as software and systems engineering around the model: data pipelines, authorization boundaries, measurement and improvement loops. An initial discussion can use a representative workflow and shareable anonymized documents to identify measurable questions. Numerical targets and deployment commitments are established only after that scope is understood.