Contents<\/strong><\/p>\n\n\n\n A new 120-page framework agreement, a short review period, an overworked legal team. This scenario is omnipresent in the B2B sector. Large Language Models (LLMs) can fundamentally change this process, provided the technical implementation is right. Those who understand the technical fundamentals will make more informed decisions – when it comes to infrastructure, risk assessment and building a sustainable solution for contract analysis. <\/p>\n<\/div>\n<\/div> Before an LLM can process a contract, it must be available in a suitable format. In practice, contracts are often only available as scanned PDFs or poorly structured Word documents. For an AI, scanned PDFs are just images without machine-readable text. <\/p>\n\n\n\n Optical Character Recognition (OCR) is used here, which recognizes characters in images and converts them into machine-readable text. Modern OCR solutions achieve high recognition rates, but errors can still occur with poor scan quality, handwritten annotations or complex tables. <\/p>\n\n\n\n Pre-processing is therefore an often underestimated part of the system. This includes the correction of OCR errors, the recognition of document structure (sections, clauses, attachments), the processing of tables and images and the normalization of date formats or currency specifications. <\/p>\n<\/div>\n<\/div> A central technical limit of LLMs is the context window. This is the maximum amount of text that a model can process per request. Current models can handle significantly more than previous generations, but extensive contracts with annexes, service specifications and framework agreements quickly exceed this limit if you try to process everything in a single query. <\/p>\n\n\n\n The solution is called Retrieval Augmented Generation (RAG). The principle is elegant: the entire contract is stored in small sections (chunks) in a vector database. Whena user<\/em>asks a question, the system first searches for the relevant text passages and transfers them to the LLM with the question. The model then answers only on the basis of these passages, not from its general training knowledge. <\/p>\n\n\n\n This has two decisive advantages. Firstly, the approach scales: the document length no longer plays a role because only relevant sections are ever loaded. Secondly, the answer is comprehensible: Every statement in the LLM can be traced back directly to a specific passage in the original document. <\/p>\n\n\n\n In practice, many systems combine RAG with a hierarchical document structure: clauses are not only saved as continuous text, but are also provided with metadata – such as clause type, contracting party or validity date. This makes targeted queries such as “Show all clauses on the contract term from supplier contracts from 2023” possible in the first place. <\/p>\n<\/div>\n<\/div> One use case in which LLMs provide particularly efficient support is the comparison of contracts. Deviations from an internal standard can be identified, as can changes made to a contract by a contractual partner during negotiations. <\/p>\n\n\n\n A simple text search quickly reaches its limits here. It finds identical words, but no similarities in content. A clause that limits liability to “direct damages” and another that “excludes consequential damages” have the same meaning in terms of content, but look completely different in terms of text. <\/p>\n\n\n\n\n
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<\/figure><\/div>\n\n\n\nFrom raw text to structured analysis: what happens before AI<\/strong><\/h2>\n\n\n\n
RAG instead of installments: The solution for long contracts<\/strong> <\/h2>\n\n\n\n
Embeddings: Precision in contract comparison<\/strong><\/h2>\n\n\n\n
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