1. Prompt Chaining Prompt chaining breaks a task down into a series of steps, where each LLM call processes the output of the previous step. 2. Routing Routing classifies the input and directs it to a specialized follow-up task. 3. Parallelization An LLM can sometimes work on a task concurrently and aggregate its outputs programmatically.

1. Base Packages Kubernetes version ≥ 1.18 socat Required conntrack Required ebtables Optional, but recommended ipset Optional, but recommended ipvsadm Optional, but recommended 1 apt-get -y install socat conntrack ebtables ipset ipvsadm 2. Port Requirements 2.1 Base Node Services Service Protocol Action Start Port End Port Comment ssh TCP allow 22 22 Node remote management docker TCP allow 2375 2376 Docker remote communication etcd TCP allow 2379 2380 etcd cluster communication 2.

Embedding Models Embedding models map high-dimensional data into a lower-dimensional space, which makes the data easier to process and analyze. Text Models Here is a leaderboard, https://huggingface.co/spaces/mteb/leaderboard The leaderboard above gives each model’s score, parameter count, memory usage, vector dimension, maximum tokens, and other information. Below are some commonly used open-source models:

1. Problems with Using LLMs Directly Unstable output One characteristic of generative AI is the diversity of its output. Ask an LLM the same question several times and you may get different answers. This uncertainty in output is a pleasant surprise for users in conversation and creative scenarios. But in

1. Memory Alignment Arrange the fields inside a struct from largest to smallest. This reduces memory footprint. Install the fieldalignment tool 1 go install golang.org/x/tools/go/analysis/passes/fieldalignment/cmd/fieldalignment@latest Analyze and fix memory alignment 1 2 3 4 5 6 7 8 9 fieldalignment -fix ./... /Users/shaowenchen/Code/app/config/config.go:136:14: struct with 32 pointer bytes could be 24 /Users/shaowenchen/Code/app/config/config.