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Why does my deployed LLM give inconsistent answers to the same prompt?
This is usually due to sampling settings rather than model instability. Parameters like temperature, top-k, and top-p introduce randomness. If these aren’t fixed, outputs will vary even for identical inputs. Set deterministic decoding for consistent responses, especially in production. Also verify tRead more
This is usually due to sampling settings rather than model instability.
Parameters like temperature, top-k, and top-p introduce randomness. If these aren’t fixed, outputs will vary even for identical inputs. Set deterministic decoding for consistent responses, especially in production. Also verify that prompts don’t include dynamic metadata like timestamps.
Common mistakes:
Leaving temperature > 0 unintentionally
Mixing deterministic and sampled decoding
Assuming reproducibility by default
Determinism must be explicitly configured.
See lessWhy does quantization reduce my model accuracy unexpectedly?
Quantization introduces approximation error. Some layers and activations are more sensitive than others. Without calibration, reduced precision distorts learned representations. Use quantization-aware training or selectively exclude sensitive layers. Common mistakes: Post-training quantization withoRead more
Quantization introduces approximation error.
Some layers and activations are more sensitive than others. Without calibration, reduced precision distorts learned representations.
Use quantization-aware training or selectively exclude sensitive layers.
Common mistakes: Post-training quantization without evaluation, quantizing embeddings blindly and ignoring task sensitivity
Compression always trades something.
See lessWhy does my model’s performance drop only during peak traffic hours?
This usually points to resource contention or degraded inference conditions rather than a modeling issue. During peak hours, models often compete for CPU, GPU, memory, or I/O bandwidth. This can lead to timeouts, truncated inputs, or fallback logic silently kicking in, all of which reduce observed pRead more
This usually points to resource contention or degraded inference conditions rather than a modeling issue.
During peak hours, models often compete for CPU, GPU, memory, or I/O bandwidth. This can lead to timeouts, truncated inputs, or fallback logic silently kicking in, all of which reduce observed performance. Check system-level metrics alongside model metrics. Look for increased latency, dropped requests, or reduced batch sizes under load. If you use autoscaling, verify that new instances warm up fully before serving traffic.
Common mistakes:
Treating performance drops as data drift without checking infrastructure
Not load-testing with realistic concurrency
Ignoring cold-start behavior in autoscaled environments
Model quality can’t be evaluated independently of the system serving it.
See lessWhy does my LLM-based system fail when user inputs get very long?
Long inputs often push the model beyond its effective attention capacity, even if they fit within the formal context limit. As prompts grow, important instructions or early context lose influence. The model technically processes the input, but practical reasoning quality degrades. The fix is to struRead more
Long inputs often push the model beyond its effective attention capacity, even if they fit within the formal context limit.
As prompts grow, important instructions or early context lose influence. The model technically processes the input, but practical reasoning quality degrades.
The fix is to structure inputs rather than just truncate them. Summarize earlier content, chunk long documents, or use retrieval-based approaches so the model only sees relevant context.
Common mistakes:
Feeding entire documents directly into prompts
Assuming larger context windows solve everything
Letting user input override system instructions
LLMs reason best with focused, curated context.
See lessWhy does my deployed model slowly become biased toward one class over time?
This usually happens when feedback loops in production reinforce certain predictions more than others. In many real systems, model outputs influence the data collected next. If one class is shown or acted upon more often, future training data becomes skewed toward that class. Over time, the model apRead more
This usually happens when feedback loops in production reinforce certain predictions more than others.
In many real systems, model outputs influence the data collected next. If one class is shown or acted upon more often, future training data becomes skewed toward that class. Over time, the model appears to “prefer” it, even if the original distribution was balanced.
To fix this, monitor class distributions in both predictions and incoming labels. Introduce sampling or reweighting during retraining so minority classes remain represented. In some systems, delaying or decoupling feedback from training helps break the loop.
Common mistakes:
Assuming bias only comes from training data. Retraining on production data without auditing it or monitoring accuracy but not class balance
Models don’t just learn from data — they learn from the systems around them.
See lessHow can monitoring only accuracy hide serious model issues?
Accuracy masks class imbalance, confidence collapse, and user impact. A model can maintain accuracy while becoming overly uncertain or biased toward majority classes. Secondary metrics reveal these issues earlier. Track precision, recall, calibration, and input drift alongside accuracy. Common mistaRead more
Accuracy masks class imbalance, confidence collapse, and user impact.
A model can maintain accuracy while becoming overly uncertain or biased toward majority classes. Secondary metrics reveal these issues earlier.
Track precision, recall, calibration, and input drift alongside accuracy.
Common mistakes:
Single-metric dashboards
Ignoring prediction confidence
No slice-based evaluation
Good monitoring is multi-dimensional.
See lessHow do I validate that my retraining pipeline is safe?
Run shadow training and compare outputs before deployment.Train the new model without serving it and compare predictions against the current model on live traffic. Large unexplained deviations are red flags. Automate validation checks and require manual approval for major shifts. Common mistakes: BlRead more
Run shadow training and compare outputs before deployment.Train the new model without serving it and compare predictions against the current model on live traffic. Large unexplained deviations are red flags.
Automate validation checks and require manual approval for major shifts.
Common mistakes:
Blind retraining schedules
No regression testing
Treating retraining as routine
Automation needs safeguards.
See lessHow do I know when to retrain versus fine-tune?
Retrain when the data distribution changes significantly; fine-tune when behavior needs adjustment. If core patterns shift, fine-tuning may not be enough. If the task remains similar but requirements evolve, fine-tuning is more efficient. Evaluate both paths on a validation set before committing. CoRead more
Retrain when the data distribution changes significantly; fine-tune when behavior needs adjustment.
If core patterns shift, fine-tuning may not be enough. If the task remains similar but requirements evolve, fine-tuning is more efficient.
Evaluate both paths on a validation set before committing.
Common mistakes:
Fine-tuning outdated models
Retraining unnecessarily
Ignoring data diagnostics
Choose the strategy that matches the change.
See lessHow can feature scaling differences silently break a retrained model?
If scaling parameters change between training runs, the model may receive inputs in a completely different range than expected. This often happens when scalers are refit during retraining instead of reused, or when training and inference pipelines compute statistics differently. The model still runsRead more
If scaling parameters change between training runs, the model may receive inputs in a completely different range than expected.
This often happens when scalers are refit during retraining instead of reused, or when training and inference pipelines compute statistics differently. The model still runs, but its learned weights no longer align with the input distribution.Always persist and version feature scalers alongside the model, or recompute them using a strictly defined window. For tree-based models this matters less, but for linear models and neural networks it’s critical.
Common mistakes:
Recomputing normalization on partial datasets
Applying per-batch scaling during inference
Assuming scaling is “harmless” preprocessing
Feature scaling is part of the model contract.
See lessHow do I detect when my model is learning spurious correlations?
Spurious correlations show up when a model performs well in validation but fails under slight input changes.This happens when the model latches onto shortcuts in the data—background artifacts, metadata, or proxy features—rather than the true signal. You’ll often see brittle behavior when conditionsRead more
Spurious correlations show up when a model performs well in validation but fails under slight input changes.This happens when the model latches onto shortcuts in the data—background artifacts, metadata, or proxy features—rather than the true signal.
You’ll often see brittle behavior when conditions change.Use counterfactual testing: modify or remove suspected features and observe prediction changes. Training with more diverse data and applying regularization also helps reduce shortcut learning.
Common mistakes:
Trusting aggregate metrics without stress tests
Training on overly clean or curated datasets
Ignoring feature importance analysis
Robust models should fail gracefully, not catastrophically.
See less