Code generation from natural language instructions in Bangla is a fundamental task in programming automation, as explored in BLP-2025 Shared Task 2: Code Generation in Bangla. Current code generation models are designed primarily for high-resource languages such as English, which creates major limitations when applied to Bangla. The key challenges are limited training data and difficulty in correctly interpreting Bangla programming instructions. In this paper, to accommodate Bangla instructions, we present a chain of thought (CoT) based prompting approach with Qwen2.5-Coder-14B model. We further introduce few-shot example in the prompt template to improve the accuracy. During competition, an accuracy of 93% is achieved in the shared test set (test_v1.csv) and 82.6% is achieved on the public and private test sets (hidden). After the competition is closed, we implement a debugger prompt technique which refines answers with 3 iterative fixing attempts. Applying this new technique on the public shared test set, our system outperforms by 7% and achieves 100% accuracy on the public test set, highlighting the effectiveness of combining CoT prompting with iterative debugging.