Harnessing LLMs for Reliable Academic Supervision: A Comparative Study
Large language models routinely produce fluent answers to single-shot prompts, yet deploying them as reliable components of a domain decision system is substantially harder. Closing this gap is the work of harness engineering: the deliberate composition of deterministic scaffolding (symbolic filters, retrieval, schema-typed I/O, LLM-as-judge loops, HITL gates, persistent state, audit trails) around an LLM core. We present a case study in academic supervision, a domain combining high-stakes recommendation, longitudinal accountability, and structured operational workflows. We compare a baselin
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- FuzzySimilar title/name (fuzzy) · 84%roboflow/supervision →
“Fuzzy title match (0.92): “Harnessing LLMs for Reliable Academic Supervision: A Compara” ≈ “roboflow/supervision””
- LinkedLinked via arxiv author · 85%Akash Raj →
“Harnessing LLMs for Reliable Academic Supervision: A Comparative Study”