With rapid advances in NLP, models are getting better at generating fluent and accurate responses.

But in real-world applications:

• Misunderstanding context still leads to incorrect outputs
• High accuracy doesn’t always mean useful results
• Domain-specific understanding often becomes the bottleneck

So the challenge seems to be shifting from just improving models to improving how they understand and use context.

From your experience:

• What creates better outcomes in NLP systems today?
• Stronger models or better context handling?

Would love to hear practical insights