Automating GOST Parameter Extraction with LLMs: A 24x Time Reduction Case Study
At a full-cycle metallurgical plant with 4,500 inventory items and over 200 regulatory documents (mostly scans of Soviet-era GOSTs and OSTs), preparing a single control sheet used to take over 2 hours. Technologists manually extracted parameters from tables, notes, and text across 40+ indicators. An LLM-based solution reduced this time to 3–5 minutes—a 24-fold improvement.
The challenge: generate a table with control parameters, sources (section/table), and values based on inventory characteristics (steel grade, billet diameter, group) and a GOST PDF.
Why a Parser Wasn't Enough
A template-based parser failed due to format diversity: in some GOSTs, parameters are in tables; in others, they're in notes or referenced sections. Semantic understanding is required, not just structural parsing.
The LLM acts as a 'virtual technologist':
- Input: GOST PDF + inventory parameters
- Prompt: list of parameters with search algorithms
- Output: table of 'Parameter — Value — Source'
The Failure of a Universal Prompt
Testing in Perplexity showed: Claude Sonnet 4.6 achieved 85% accuracy on the first GOST, GPT 5.4 achieved 72% (in Thinking mode). But errors recurred on subsequent documents due to structural differences—nested tables, constants, ranges.
A universal approach doesn't scale. Transition to prompts tailored to specific GOSTs.
Working Architecture Based on the Pareto Principle
80% of inventory is covered by 18% of GOSTs (20 key documents for the pilot).
For each—a custom prompt with rules:
- Parameter location (table/section)
- Interpretation of edge cases ('not less than', ranges)
- Table processing
Process:
- Feed GOST PDF and parameters into the prompt.
- Receive results table.
- If errors occur—analyze in Perplexity with the command 'Update rule XX'.
9 iterations over 14 days ensured 100% accuracy on pilot GOSTs.
Current Implementation and Scaling
Rules are exported to an Excel table for technologists to edit. The prompt loads the table + input data, outputs a ready table for the enterprise system.
Adding new GOSTs: copy template rules, debug in 1–2 iterations.
Key takeaways:
- Modern LLMs (Claude Sonnet) reliably parse PDF scans with tables of any complexity.
- Apply Pareto: start with the top 20% of documents.
- Custom prompts per document are more effective than universal ones.
- Iterative debugging eliminates systemic errors (nested tables).
Takeaways for IT Professionals
This approach is applicable in industries with regulatory documentation:
- Metallurgy, mechanical engineering
- Chemical industry, construction, energy
- Pharmaceuticals (without confidential data)
Success conditions:
- Manual data transfer from heterogeneous PDFs.
- No strict confidentiality requirements.
- Ability for iterative prompt refinement.
— Editorial Team
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