In the final installment of a series on Practical BM25, the focus is on optimizing the parameters b and k1 for Elasticsearch's similarity ranking algorithm. While the default values of b = 0.75 and k1 = 1.2 generally work well, fine-tuning these parameters is suggested only after exploring other relevancy improvements like boosting, synonyms, and fuzziness. The article details how b and k1 should be adjusted based on document length and term saturation, with b ranging from 0 to 1 and k1 from 0 to 3, depending on the corpus. It underscores the importance of experimentation and testing across various queries and documents when adjusting these parameters, using tools like Elasticsearch's Rank Eval API and Explain API to aid in this process. The piece concludes by highlighting that while BM25 is effective, no single algorithm or parameter set is universally optimal, and most tuning efforts should focus on Elasticsearch's expressive query language, index controls, and user feedback, with BM25 parameter adjustments as a deeper level of refinement.