1. Purpose and Objective:
Study the effect of incorporating chain-of-thought techniques into large language models in completing designated tasks. The principal objective is to assess how planning and structuring responses using these techniques improve the model's performance and output quality.

2. Scope:
The study covers the application of chain-of-thought techniques, including outlining, drafts, and refinement iterations, in recent large language model operations in a range of tasks and domains. 

3. Methodology:
a. Define the Test Set: Prepare a set of tasks and domains where larger language models are tested. The tasks should allow the comparison of the model's performance with and without using chain-of-thought techniques.
b. Approach Implementation: Implement chain-of-thought techniques within the language models. Record how each technique is applied and its influence on output quality.
c. Benchmark Testing: Assess the language model's performance without chain-of-thought techniques. This assessment forms a control set against which the technique's effects could be judged.
d. Performance Assessment: Evaluate the performance of language models that utilize chain-of-thought techniques. Use the same set of tasks that were tested in the benchmark tests.
e. Comparative Analysis: Compare results from the benchmark testing and performance assessment. Determine any performance improvements, response quality enhancements, and efficiency gains.

4. Expected Results:
Expect to gain insights into the utility and efficiency of incorporating chain-of-thought techniques in large language models. The study may demonstrate the techniques' impact on improving the quality of output, enhancing interpretability, and boosting overall task performance.

5. Conclusion:
The examination of the chain-of-thought techniques' effect on large language model tasks aims to shed light on potential model optimization. Depending on the results, the findings may steer future direction in AI language model development.