A daily crypto, finance, and AI vocabulary grouping puzzle covering reserve checks, execution quality, model training, and prompting workflows.
Stablecoin Reserve Coverage
These words describe how a dollar-linked token shows backing, coverage, and stability.
Reserve CoverageBacking RatioPeg StabilityRedemption Flow
- Reserve Coverage: Reserve coverage measures how much backing supports the liabilities of a stablecoin or similar token.
- Backing Ratio: A backing ratio compares reserve assets with the claims they are meant to support.
- Peg Stability: Peg stability describes how closely a token holds its target value over time.
- Redemption Flow: Redemption flow is the path a holder follows to exchange a token back into its reference asset.
DeFi Execution Routing
These words cover routing logic that tries to improve fills and reduce execution cost.
Best ExecutionLiquidity RoutingOrder SplittingPrice Improvement
- Best Execution: Best execution is the practice of aiming for the best available trade outcome after fees, spreads, and slippage.
- Liquidity Routing: Liquidity routing is the process of directing orders to venues or pools with available depth.
- Order Splitting: Order splitting divides a larger order into smaller pieces to reduce impact and improve fills.
- Price Improvement: Price improvement happens when a trade executes at a better price than the one initially quoted.
AI Model Training Core
These words describe the core knobs and checkpoints used while fitting a model.
CheckpointGradient DescentLearning RateOptimizer
- Checkpoint: A checkpoint is a saved snapshot of a model's weights and training progress.
- Gradient Descent: Gradient descent is an optimization method that nudges model parameters toward lower error.
- Learning Rate: The learning rate controls how large each parameter update is during training.
- Optimizer: An optimizer applies the rules that update a model's parameters while it learns.
Prompting and Evaluation
These words cover prompt design, example selection, and how model answers get reviewed.
Prompt EngineeringFew-ShotIn-Context LearningReward Model
- Prompt Engineering: Prompt engineering is the practice of writing and refining instructions to guide a model's output.
- Few-Shot: Few-shot prompting supplies a small number of examples so the model can imitate the pattern.
- In-Context Learning: In-context learning is a model's ability to pick up a pattern from examples inside the prompt.
- Reward Model: A reward model scores outputs so a system can prefer responses that better match a goal or rubric.