Continual Learning: Building AI That Gets Better With Every Task
From expert feedback to auto-optimization, and why 2026 is the year everything changes
By Sasank Aduri, Aidan O'Gara, and Derek Parham
DeepMind researchers recently predicted: "2026 will be the year of continual learning."
This isn't just a research trend. It's the key to unlocking actual ROI from enterprise AI.
Current AI deployments are frozen in time. The model you deploy today is exactly the model you'll have in a year. It doesn't learn from your corrections. It doesn't adapt to your processes. It doesn't get better at being your AI.
This is why most enterprise AI fails to deliver. The demo impressed, but the production system doesn't improve. After six months, you're still correcting the same mistakes, still providing the same context, still frustrated that the AI doesn't "get it."
Continual learning changes this. AI that learns from feedback. Systems that improve with use. Workflows that auto-optimize based on what works.
This post covers how we're building continual learning into Context, what we've learned from production deployments, and the technical approaches making this possible.
The Problem: Static Models in Dynamic Organizations
Every deployed AI model represents a snapshot:
- Training data from a specific time period
- Optimization for specific benchmarks
- No knowledge of your organization
- No exposure to your processes
The moment you deploy, the model starts falling behind:
- Your processes evolve; the model doesn't know
- Your preferences clarify; the model can't learn them
- Your team develops best practices; the model never sees them
- Market conditions change; the model is stuck in the past
This is the "frozen model" problem. And it's why AI that impressed in demos often disappoints in production.
What Continual Learning Means in Practice
Continual learning in LLMs means a deployed system can keep improving—learning new domains, adapting to changing requirements, incorporating feedback—without full retraining and without losing previous capabilities.
The "without losing previous capabilities" part is crucial. This is what makes continual learning hard.
Catastrophic Forgetting
The core challenge: when you update a model on new data, it tends to forget old data. The new updates modify the same parameters that supported earlier abilities.
If you fine-tune a general model on legal domain data, it might become great at law but forget how to write marketing copy. Tune it further on your company's legal style, and it might forget general legal knowledge.
This is catastrophic forgetting, and it's been the central problem in continual learning research for decades.
Recent research has shifted from "it forgets because it overwrites" to "what about optimization makes it overwrite." This has led to practical mitigation strategies.
Replay
The first defense against forgetting is replay: while learning new things, keep practicing old things.
When training on your company's legal procedures, include examples of general tasks the model should still perform. This prevents old capabilities from decaying.
Modern approaches use small, strategic replay—not storing everything, but keeping "anchor behaviors" that must never be lost.
Parameter-Efficient Methods
Instead of updating all model weights, add small adapter modules and only train those. The base model stays frozen; new capabilities live in new parameters.
This is the intuition behind LoRA and its variants. New capabilities plug in without disturbing existing ones.
The tradeoff: limited capacity for new learning. But for most enterprise adaptations, the capacity is sufficient.
Elastic Weight Consolidation
Identify which parameters are important for old capabilities and constrain how much they can change when learning new things. Less important parameters can update freely.
This requires tracking parameter importance across tasks—overhead, but effective at preventing forgetting.
Context's Continual Learning Architecture
We've built continual learning into Context through three integrated mechanisms.
1. Learning from Expert Feedback (Context Evals)
Every task in Context Workspace can receive expert feedback through Context Evals:
- What aspects were good?
- What aspects needed improvement?
- What would have been better?
This feedback is captured with full context—not just "this was wrong" but the complete situation, the output, and the specific improvement needed.
Over time, this builds a corpus of expert-validated examples. These become:
- Demonstration data: Examples of good outputs for similar situations
- Contrastive pairs: (wrong output, corrected output) pairs for preference learning
- Rubric refinement: Understanding of what "good" means in this domain
The system learns what YOUR experts consider good, not generic quality criteria.
2. Workflow Auto-Optimization
Context Workspace captures workflows—the multi-step processes for accomplishing tasks. As these workflows execute repeatedly, patterns emerge:
- Which steps consistently succeed?
- Where do human corrections happen?
- What context improves outcomes?
- What prompts produce better results?
We use these patterns to automatically optimize workflows:
Prompt optimization: Using techniques from DSPy and MIPROv2 to refine prompts based on outcome data. The system automatically generates prompt variations, tests them, and evolves toward better-performing versions.
Context selection: Learning which context actually helps for which tasks. Over time, the system gets better at retrieving relevant information and filtering noise.
Step refinement: Adjusting the workflow steps themselves—adding verification steps where errors are common, removing unnecessary steps, reordering for efficiency.
3. Context Evals Benchmarking
Generic benchmarks (MMLU, HumanEval, etc.) tell you about general model capability. They tell you nothing about capability on YOUR tasks.
Context Evals builds organization-specific benchmarks:
- Golden sets: Curated examples of successful task completions
- Rubrics: Multi-dimensional evaluation criteria specific to your domain
- Regression detection: Alerts when performance drops on established capabilities
When you update models or workflows, you can evaluate against your actual use cases, not proxy benchmarks.
This closes the loop: feedback → learning → evaluation → feedback.
Technical Implementation
ACE (Automatic Curriculum for Embodied agents)
ACE principles inform how we sequence learning. Instead of training on all feedback equally, we curriculum-order:
- Start with clear cases where feedback is unambiguous
- Progress to harder cases as the system improves
- Focus learning on areas with highest error rates
This accelerates learning and improves stability.
DSPy-Style Optimization
DSPy treats LLM programs as optimizable. Prompts, demonstrations, and pipeline structure can all be tuned based on outcome metrics.
We've integrated this approach:
- Workflows are treated as programs with tunable components
- Outcome metrics (from Context Evals) drive optimization
- The system automatically searches for better configurations
MIPROv2 for Prompt Generation
MIPROv2 (Model-based Iterative Prompt Refinement and Optimization) generates candidate prompts and systematically tests them.
In our implementation:
- The system generates prompt variations
- Variations are tested against golden sets
- Winning variations are deployed
- The cycle continues automatically
This means prompts improve without manual engineering.
Retrieval-Augmented Learning
Instead of trying to bake everything into model weights, we use retrieval to provide relevant examples:
- Similar past tasks and their successful completions
- Expert feedback on related situations
- Institutional knowledge relevant to the current task
The model accesses this context at inference time. Learning becomes about improving what gets retrieved, not just what's in weights.
What We've Seen in Production
Enterprise Case Study
At a leading semiconductor company, our approach jumped task accuracy from 23% to over 90% on specific engineering workflows.
The models didn't suddenly learn chip engineering. They learned that company's specific processes, signals, and institutional knowledge. Through months of expert feedback and workflow refinement, the system became an effective engineer for that organization, not just a general-purpose AI.
The Compound Effect
Organizations using Context for months have dramatically better AI than those just starting.
This isn't because we shipped better models to early customers. It's because their systems have accumulated:
- Months of expert feedback
- Refined workflows optimized for their use cases
- Organization-specific benchmarks
- Captured institutional knowledge
This creates a compounding advantage. Every week of use makes the system better.
The Flywheel
We're seeing a flywheel effect:
- Better AI → More usage
- More usage → More feedback
- More feedback → Better AI
- Repeat
Organizations that commit to the feedback loop see accelerating returns. Those that treat AI as a static tool see stagnation.
The 2026 Prediction
DeepMind predicts 2026 is the year of continual learning. Here's why that matters for enterprises:
The capability gap will widen: Organizations that have been building continual learning systems will have capabilities that can't be replicated by starting fresh with better models.
Static deployment will become obsolete: "Deploy once and forget" will be recognized as leaving most of the value on the table.
Feedback infrastructure becomes critical: The organizations that captured the most expert feedback will have the best AI. This makes feedback capture a strategic priority.
Custom models become viable: Eventually, the accumulated feedback and domain adaptation will enable training genuinely custom models that can't be replicated by competitors.
Getting Started
The path to continual learning starts with feedback infrastructure:
- Deploy in real workflows: AI must be doing real work to generate real feedback signals
- Capture structured feedback: Build the habit of expert validation and correction
- Accumulate golden examples: Curate successful task completions as benchmarks
- Enable optimization loops: Let the system automatically improve based on feedback
This isn't a feature you turn on. It's a capability you build through consistent investment in feedback quality and volume.
The models will keep getting better. What won't automatically appear is the feedback data that makes them work for YOUR organization. That's what you build with continual learning infrastructure.
Context Evals is the continual learning layer for enterprise AI. Learn more at context.inc
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