Detailed Analysis
A non-lawyer Claude Pro user reports significant performance degradation after scaling a legal case research project beyond 60 uploaded files, describing symptoms of contextual drift and diminished big-picture reasoning that were largely absent when the project contained fewer than 30 files. The project involves a diverse mix of document types — PDFs of email chains and reports, photographs, and markdown files — organized across multiple chats segmented by evidence avenue, with a defined project objective. The user notes that Claude was performing adequately at lower file volumes but now frequently "forgets facts" and struggles to synthesize the broader narrative of the case, prompting a request for practical guidance from other Pro users.
The core issue the user is encountering relates to how Claude's Projects feature manages and prioritizes context. While Projects are designed to persist files and instructions across conversations, they do not guarantee that all uploaded material is actively held in the model's working context window during any given exchange. As the file count grows, the system must make decisions about what to surface and weight, and with 60+ heterogeneous documents, the density of potentially relevant information creates retrieval and attention challenges. The problem is compounded by the user's multi-chat structure: evidence siloed across separate conversations may prevent Claude from drawing cross-thread connections that would otherwise support big-picture analysis.
This situation illustrates a well-documented tension in large language model deployment between document volume and coherent reasoning quality. Context windows, even in frontier models, are finite, and as the ratio of relevant signal to total uploaded material shifts — particularly with mixed media types like photographs alongside dense PDFs — the model's ability to maintain a unified, accurate mental model of the case degrades. The user's experience essentially describes a form of context saturation, where sheer file count undermines the synthesis that made the tool valuable at smaller scale.
The phenomenon points to broader challenges in applying general-purpose AI assistants to high-stakes, document-intensive professional workflows. Legal work, in particular, demands not just retrieval of discrete facts but the layered, relational reasoning that connects those facts into coherent arguments — a task that becomes progressively harder for AI systems as document complexity and volume increase. The user's instinct to segment chats by evidence avenue, while organizationally logical, may inadvertently fragment the connective tissue the model needs to reason holistically about the case.
Practical mitigations in this scenario typically involve deliberate context management rather than passive file accumulation: creating structured summary documents (such as master markdown timelines or evidence index files) that synthesize key facts from multiple source documents, reducing the model's dependence on raw file retrieval for big-picture questions. Pinning a well-maintained project objective and case summary as a persistent instruction, rather than relying on Claude to reconstruct that framing from raw files, also tends to improve coherence. This case underscores a wider industry challenge: as users push AI tools into increasingly complex, high-volume professional applications, the gap between what the technology can do in a demo environment and what it can sustain reliably at production scale becomes consequential — particularly in domains like legal work where factual precision is not optional.
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