Apply for AI Grants India

Reliable AI is not produced by chasing a higher accuracy score on a test set. AI model accuracy improvement is a disciplined process of defining the business error that matters, building representative data, selecting suitable evaluation metrics, and monitoring behaviour after deployment. For Indian teams, this also means accounting for multilingual inputs, uneven connectivity, regional variation, privacy obligations, and limited compute budgets.

Start with the right definition of accuracy

“Accuracy” has different meanings across classification, regression, generative AI, and computer vision. A fraud model may need high recall because missed fraud is expensive. A medical triage system may prioritise sensitivity and calibrated risk estimates. A customer-support assistant must be judged on factuality, instruction-following, language coverage, and safe escalation—not only whether its response resembles a reference answer.

Before changing the model, write down:

• The decision the model supports.
• The cost of false positives and false negatives.
• The users, languages, devices, and operating conditions it must handle.
• The minimum acceptable performance for each important segment.
• The action taken when confidence is low.

This prevents teams from optimising a convenient metric that does not represent product quality.

Build a trustworthy evaluation set

A model cannot outperform the data used to teach and test it. Begin with an audit of labels, duplicates, missing values, leakage, class balance, and sampling bias. Separate training, validation, and test data before extensive experimentation. If multiple records come from the same person, device, location, or document, split them by entity where appropriate; otherwise, the test score may be unrealistically high.

Your evaluation set should include:

• Common cases, rare but costly cases, and difficult borderline examples.
• Data from different Indian regions, scripts, accents, and code-mixed language patterns.
• Realistic image quality, lighting, noise, network, and device variation.
• A fixed “golden set” for regression testing after every model or prompt change.
• A challenge set designed around known failure modes.

For language and vision systems, human review remains essential. Record the annotation guidelines, reviewer agreement, and reasons for disagreement. If you are building a regional-language application, compare performance across languages rather than reporting one aggregate number. Work involving Indic vision-language systems can also benefit from the considerations in open-source vision-language models for Indian languages.

Choose metrics that expose failure

For imbalanced classification, accuracy can hide poor performance on the minority class. Use a metric set that matches the decision:

• Precision: Of the positive predictions, how many are correct?
• Recall: Of the real positives, how many did the model find?
• F1 score: A balance between precision and recall when both matter.
• Specificity: How well the model rejects negatives.
• PR-AUC: Often more informative than ROC-AUC for rare positive events.
• Confusion matrix: Shows the actual pattern of errors by class.
• MAE and RMSE: Useful for regression, with different sensitivity to large errors.
• Calibration: Whether predicted probabilities match observed outcomes.

Report confidence intervals where possible, along with results by segment. A small gain that is not statistically or operationally meaningful should not justify a costly redeployment.

Improve the data before increasing model complexity

Data work frequently delivers larger gains than switching architectures. Remove contradictory labels, standardise units and formats, and investigate suspiciously easy examples. For image data, check cropping, resolution, lighting, and annotation boundaries. For text, inspect OCR errors, transliteration, spelling variation, and duplicated content.

Useful interventions include:

• Targeted labelling: Label examples where the current model is uncertain or wrong.
• Hard-negative mining: Add realistic examples that look like the target class but are not.
• Class balancing: Use resampling or class weights carefully; preserve the real-world distribution in evaluation.
• Augmentation: Simulate noise, blur, spelling variation, or channel changes without creating unrealistic samples.
• Feature engineering: Add domain-relevant signals, while avoiding proxies that create unfair outcomes.
• Leakage checks: Remove features unavailable at prediction time.

For teams building computer vision products, a structured dataset and deployment pipeline matter as much as architecture; the workflow in how to build computer vision models on GitHub provides a useful reference for reproducibility.

Tune the model systematically

Establish a simple baseline first. Compare a linear model, decision tree, or small pretrained model before investing in a larger system. This reveals whether the problem is data-limited, capacity-limited, or poorly framed.

Then evaluate changes one at a time where possible:

• Tune learning rate, batch size, regularisation, depth, and decision thresholds.
• Use stratified or group-aware cross-validation when the data permits it.
• Apply early stopping and track validation performance to limit overfitting.
• Use class weights or cost-sensitive learning when errors have unequal consequences.
• Compare fine-tuning, retrieval, prompting, and smaller specialist models for LLM applications.
• Test quantisation and distillation only after confirming that quality loss is acceptable.

For on-device or low-bandwidth products, accuracy must be measured alongside latency, memory, battery use, and failure behaviour. See the AI model optimisation guide for mobile devices before treating compression as a purely technical afterthought.

Prevent overfitting and validate robustness

Overfitting occurs when a model memorises training patterns that do not generalise. Use held-out data, regularisation, augmentation, simpler architectures, and leakage-resistant splits. Underfitting may require better features, a more expressive model, longer training, or improved labels—not merely more hyperparameter searches.

Run robustness tests before launch. Perturb inputs, test missing fields, vary image quality, and evaluate out-of-distribution examples. For generative systems, test prompt injection, unsupported questions, citation accuracy, repetition, and refusal behaviour. If the application uses a large language model locally, compare resource constraints and output quality using the practices outlined in how to deploy large language models locally.

Monitor accuracy after deployment

Production data changes. New products, seasonal behaviour, policy changes, camera upgrades, and language trends can create drift even when infrastructure is healthy. Track:

• Input and label distribution drift.
• Segment-level precision, recall, and calibration.
• Abstention, escalation, correction, and user-feedback rates.
• Latency, timeout, token, and infrastructure costs.
• Data-quality failures and changes in missing-value patterns.

Create alerts with clear owners and thresholds. Use shadow deployments, canary releases, and rollback paths for material model changes. Retraining should be triggered by evidence—performance decay, new labelled data, or a known change in the operating environment—not by an arbitrary calendar alone.

A practical improvement loop

Use this repeatable cycle:

1. Define the decision and cost of each error.
2. Establish a baseline and segment-level evaluation set.
3. Diagnose errors by category, not just by score.
4. Fix labels, coverage, features, or prompts that explain the errors.
5. Tune and compare models using reproducible experiments.
6. Stress-test quality, fairness, security, latency, and cost.
7. Release gradually and monitor real outcomes.
8. Feed verified failures back into the next dataset version.

The strongest improvement programmes treat accuracy as a product and governance responsibility, not a single machine-learning task. For Indian builders, that means designing for diverse users from the first dataset, measuring what matters in deployment, and choosing models that the team can operate reliably at scale.