Bioprinting workflows often require repeated trial-and-error to achieve acceptable print quality, while relevant process knowledge and parameter ranges are dispersed across a rapidly growing literature. General-purpose language models can assist with scientific questions, but their output may be difficult to verify and can include unsupported claims. In this work, we present BioPrint-LKM, a bioprinting large knowledge model (LKM) implemented using a retrieval-augmented generation pipeline with citation grounding to provide traceable, evidence-based responses for bioprinting tasks. A curated knowledge base was constructed from 621 bioprinting papers, which were converted to text, segmented into passages, embedded into a vector index, and retrieved using exact nearest-neighbor similarity search. Retrieved passages were assembled into an augmented context and used to constrain generation under a domain prompt guideline that enforces source and page-level citation. The LKM was evaluated using 30 bioprinting-related questions and three large language model (LLM) backbones (GPT-4o, Claude-Sonnet-4.6, and Gemini-2.5-Flash). Across the tested 30-question benchmark, retrieval and citation grounding improved question-answer accuracy compared with LLM-only baselines by an average of 24.7%, particularly for queries requiring paper specific details such as component concentrations, mixing ratios, and instrument settings. A retrieval hyperparameter sweep further showed that top-k and passage length affect both evidence coverage and noise, with an intermediate passage length providing the best overall performance. Beyond knowledge retrieval, the LKM was applied to bioprinting process setup by generating initial parameter sets that were used to warm-start multi-objective Bayesian optimization toward target filament width and height. Compared with manual initialization, BioPrint-LKM-assisted initialization reduced the number of calibration trials by an average of 36.0% and supported downstream printing demonstrations, including conductive cell-laden hydrogel lines and an anatomical ear model. These results suggest that BioPrint-LKM, a citation grounded LKM, can serve as a practical lab-assistive tool to accelerate bioprinting setup and improve reproducibility.