LLM Context Conditioning and PWP Prompting for Multimodal Validation of
  Chemical Formulas
Authors: name: Evgeny Markhasin

Identifying subtle technical errors within complex scientific and technical
documents, especially those requiring multimodal interpretation (e.g., formulas
in images), presents a significant hurdle for Large Language Models (LLMs)
whose inherent error-correction tendencies can mask inaccuracies. This
exploratory proof-of-concept (PoC) study investigates structured LLM context
conditioning, informed by Persistent Workflow Prompting (PWP) principles, as a
methodological strategy to modulate this LLM behavior at inference time. The
approach is designed to enhance the reliability of readily available,
general-purpose LLMs (specifically Gemini 2.5 Pro and ChatGPT Plus o3) for
precise validation tasks, crucially relying only on their standard chat
interfaces without API access or model modifications. To explore this
methodology, we focused on validating chemical formulas within a single,
complex test paper with known textual and image-based errors. Several prompting
strategies were evaluated: while basic prompts proved unreliable, an approach
adapting PWP structures to rigorously condition the LLM's analytical mindset
appeared to improve textual error identification with both models. Notably,
this method also guided Gemini 2.5 Pro to repeatedly identify a subtle
image-based formula error previously overlooked during manual review, a task
where ChatGPT Plus o3 failed in our tests. These preliminary findings highlight
specific LLM operational modes that impede detail-oriented validation and
suggest that PWP-informed context conditioning offers a promising and highly
accessible technique for developing more robust LLM-driven analytical
workflows, particularly for tasks requiring meticulous error detection in
scientific and technical documents. Extensive validation beyond this limited
PoC is necessary to ascertain broader applicability.